├── fraudar ├── py.typed ├── export │ ├── __init__.py │ ├── MinTree.py │ ├── README │ └── greedy.py ├── __init__.py └── graph.py ├── docs ├── source │ ├── _static │ │ └── image.png │ ├── modules │ │ ├── fraudar.rst │ │ └── fraudar.export.rst │ ├── conf.py │ └── index.rst └── Makefile ├── .gitmodules ├── .github └── workflows │ ├── publish.yaml │ ├── python-lib.yaml │ └── static.yaml ├── tests ├── __init__.py ├── test_product.py ├── conftest.py └── test_review_graph.py ├── .pre-commit-config.yaml ├── README.rst ├── sample.py ├── pyproject.toml ├── .gitignore ├── LICENSE-2.0 └── COPYING /fraudar/py.typed: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /docs/source/_static/image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rgmining/fraudar/HEAD/docs/source/_static/image.png -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "docs/source/_templates"] 2 | path = docs/source/_templates 3 | url = https://github.com/rgmining/doc-template.git 4 | -------------------------------------------------------------------------------- /docs/source/modules/fraudar.rst: -------------------------------------------------------------------------------- 1 | fraudar package 2 | =============== 3 | 4 | .. automodule:: fraudar 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | 9 | Submodules 10 | ---------- 11 | 12 | fraudar.graph module 13 | -------------------- 14 | 15 | .. automodule:: fraudar.graph 16 | :members: 17 | :undoc-members: 18 | :show-inheritance: 19 | -------------------------------------------------------------------------------- /.github/workflows/publish.yaml: -------------------------------------------------------------------------------- 1 | name: Publish Python library 2 | on: 3 | push: 4 | tags: 5 | - "v*.*.*" 6 | jobs: 7 | publish: 8 | runs-on: ubuntu-latest 9 | steps: 10 | - name: Checkout 11 | uses: actions/checkout@v4 12 | - name: Install poetry 13 | run: pipx install poetry 14 | - name: Setup Python 15 | uses: actions/setup-python@v5 16 | with: 17 | python-version: "3.13" 18 | cache: poetry 19 | - name: Install dependencies 20 | run: poetry install 21 | - name: Build and release 22 | run: | 23 | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} 24 | poetry publish --build 25 | -------------------------------------------------------------------------------- /.github/workflows/python-lib.yaml: -------------------------------------------------------------------------------- 1 | name: Python library 2 | on: 3 | push: 4 | branches: [main] 5 | pull_request: 6 | jobs: 7 | build: 8 | strategy: 9 | fail-fast: false 10 | matrix: 11 | python-version: ["3.11", "3.12", "3.13", "3.14"] 12 | runs-on: ubuntu-latest 13 | steps: 14 | - name: Checkout 15 | uses: actions/checkout@v5 16 | - name: Setup Python 17 | uses: actions/setup-python@v6 18 | with: 19 | python-version: ${{ matrix.python-version }} 20 | pip-install: poetry 21 | - name: Install dependencies 22 | run: poetry install 23 | - uses: actions/cache@v5 24 | with: 25 | path: ~/.cache/pre-commit 26 | key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} 27 | - name: Check files 28 | run: poetry run pre-commit run --all-files 29 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | # 2 | # __init__.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | """Provide unit tests for fraudar package.""" 22 | -------------------------------------------------------------------------------- /docs/source/modules/fraudar.export.rst: -------------------------------------------------------------------------------- 1 | fraudar.export package 2 | ====================== 3 | 4 | .. automodule:: fraudar.export 5 | :members: 6 | :undoc-members: 7 | :show-inheritance: 8 | 9 | Submodules 10 | ---------- 11 | 12 | fraudar.export.MinTree module 13 | ----------------------------- 14 | 15 | .. automodule:: fraudar.export.MinTree 16 | :members: 17 | :undoc-members: 18 | :show-inheritance: 19 | 20 | fraudar.export.greedy module 21 | ---------------------------- 22 | 23 | .. automodule:: fraudar.export.greedy 24 | :members: 25 | :undoc-members: 26 | :show-inheritance: 27 | 28 | fraudar.export.run\_greedy module 29 | --------------------------------- 30 | 31 | .. automodule:: fraudar.export.run_greedy 32 | :members: 33 | :undoc-members: 34 | :show-inheritance: 35 | 36 | fraudar.export.testMinTree module 37 | --------------------------------- 38 | 39 | .. automodule:: fraudar.export.testMinTree 40 | :members: 41 | :undoc-members: 42 | :show-inheritance: 43 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/pre-commit/pre-commit-hooks 3 | rev: v6.0.0 4 | hooks: 5 | - id: check-merge-conflict 6 | - id: check-yaml 7 | - id: check-toml 8 | - id: end-of-file-fixer 9 | - id: trailing-whitespace 10 | - repo: https://github.com/google/yamlfmt 11 | rev: v0.19.0 12 | hooks: 13 | - id: yamlfmt 14 | - repo: https://github.com/tox-dev/pyproject-fmt 15 | rev: v2.11.0 16 | hooks: 17 | - id: pyproject-fmt 18 | - repo: https://github.com/astral-sh/ruff-pre-commit 19 | rev: v0.14.1 20 | hooks: 21 | - id: ruff 22 | args: [--fix] 23 | - id: ruff-format 24 | - repo: https://github.com/pre-commit/mirrors-mypy 25 | rev: v1.18.2 26 | hooks: 27 | - id: mypy 28 | args: [] 29 | additional_dependencies: ["numpy>=2.2", "scikit-learn>=1.6.1", "pytest>=8.3.5", "click>=8", "rgmining-synthetic-dataset>=0.9"] 30 | - repo: https://github.com/python-poetry/poetry 31 | rev: "2.2.1" 32 | hooks: 33 | - id: poetry-check 34 | - repo: local 35 | hooks: 36 | - id: pytest 37 | name: Run pytest 38 | entry: poetry run pytest 39 | language: system 40 | pass_filenames: false 41 | always_run: true 42 | -------------------------------------------------------------------------------- /.github/workflows/static.yaml: -------------------------------------------------------------------------------- 1 | name: Build and deploy docs to Pages 2 | on: 3 | push: 4 | tags: 5 | - "v*.*.*" 6 | workflow_dispatch: 7 | concurrency: 8 | group: "pages" 9 | cancel-in-progress: true 10 | jobs: 11 | build: 12 | runs-on: ubuntu-latest 13 | steps: 14 | - name: Checkout 15 | uses: actions/checkout@v4 16 | with: 17 | submodules: true 18 | - name: Install poetry 19 | run: pipx install poetry 20 | - name: Setup Python 21 | uses: actions/setup-python@v5 22 | with: 23 | python-version: "3.13" 24 | cache: poetry 25 | - name: Install dependencies 26 | run: | 27 | sudo apt install graphviz 28 | poetry install --with docs 29 | - name: Build pages 30 | run: make -C docs html 31 | - name: Upload artifact 32 | uses: actions/upload-pages-artifact@v3 33 | with: 34 | path: docs/build/html/ 35 | deploy: 36 | needs: build 37 | permissions: 38 | pages: write 39 | id-token: write 40 | environment: 41 | name: github-pages 42 | url: ${{ steps.deployment.outputs.page_url }} 43 | runs-on: ubuntu-latest 44 | steps: 45 | - name: Deploy to GitHub Pages 46 | id: deployment 47 | uses: actions/deploy-pages@v4 48 | -------------------------------------------------------------------------------- /tests/test_product.py: -------------------------------------------------------------------------------- 1 | # 2 | # test_product.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | import numpy as np 22 | from numpy.testing import assert_almost_equal 23 | 24 | from fraudar import ReviewGraph 25 | 26 | 27 | def test_summary(review_graph: ReviewGraph) -> None: 28 | """Test summary property.""" 29 | assert_almost_equal( 30 | review_graph.products[2].summary, 31 | np.mean(list(review_graph.reviews[review_graph.products[2]].values())), 32 | ) 33 | 34 | review_graph.reviewers[0].anomalous_score = 1 35 | assert_almost_equal( 36 | review_graph.products[2].summary, 37 | review_graph.reviews[review_graph.products[2]][ 38 | review_graph.reviewers[1] 39 | ], 40 | ) 41 | 42 | for r in review_graph.reviewers: 43 | r.anomalous_score = 1 44 | assert_almost_equal( 45 | review_graph.products[2].summary, 46 | np.mean(list(review_graph.reviews[review_graph.products[2]].values())), 47 | ) 48 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | A wrapper of FRAUDAR algorithm 2 | ============================== 3 | 4 | |GPLv3| |Build Status| |Release| 5 | 6 | |Logo| 7 | 8 | This package implements a wrapper of 9 | `FRAUDAR `__ 10 | algorithm to provide APIs defined in `Review Graph Mining 11 | project `__. 12 | 13 | Installation 14 | ------------ 15 | 16 | Use ``pip`` to install this package. 17 | 18 | .. code:: shell 19 | 20 | $pip install --upgrade rgmining-fraudar 21 | 22 | License 23 | ------- 24 | 25 | This software is released under The GNU General Public License Version 26 | 3, see 27 | `COPYING `__ 28 | for more detail. 29 | 30 | The original FRAUDAR source code is made by `Bryan 31 | Hooi `__, `Hyun Ah 32 | Song `__, `Alex 33 | Beutel `__, `Neil 34 | Shah `__, `Kijung 35 | Shin `__, and `Christos 36 | Faloutsos `__, and licensed under 37 | `Apache License, Version 2.0 `__. It is available at 38 | https://bhooi.github.io/projects/fraudar/index.html. 39 | 40 | .. |GPLv3| image:: https://img.shields.io/badge/license-GPLv3-blue.svg 41 | :target: https://www.gnu.org/copyleft/gpl.html 42 | .. |Build Status| image:: https://github.com/rgmining/fraudar/actions/workflows/python-lib.yaml/badge.svg 43 | :target: https://github.com/rgmining/fraudar/actions/workflows/python-lib.yaml 44 | .. |Release| image:: https://img.shields.io/badge/release-0.7.6-brightgreen.svg 45 | :target: https://pypi.org/project/rgmining-fraudar/ 46 | .. |Logo| image:: https://rgmining.github.io/fraudar/_static/image.png 47 | :target: https://rgmining.github.io/fraudar/ 48 | -------------------------------------------------------------------------------- /fraudar/export/__init__.py: -------------------------------------------------------------------------------- 1 | # 2 | # __init__.py 3 | # 4 | # Copyright (c) 2016-2023 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | # This file was originally made by Bryan Hooi et al, 22 | # and distributed under Apache License, Version 2.0 at 23 | # https://github.com/bhooi/bhooi.github.io/tree/master/code/camo. 24 | # 25 | """ 26 | FRAUDAR: Bounding Graph Fraud in the Face of Camouflage 27 | Authors: Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos 28 | 29 | This software is licensed under Apache License, Version 2.0 (the "License"); 30 | you may not use this file except in compliance with the License. 31 | You may obtain a copy of the License at 32 | 33 | http://www.apache.org/licenses/LICENSE-2.0 34 | 35 | Unless required by applicable law or agreed to in writing, software 36 | distributed under the License is distributed on an "AS IS" BASIS, 37 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 38 | See the License for the specific language governing permissions and 39 | limitations under the License. 40 | 41 | Version: 1.1 42 | Date: June 12, 2018 43 | Main Contact: Bryan Hooi (bhooi@andrew.cmu.edu) 44 | """ 45 | 46 | __version__ = "1.1" 47 | -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | # 2 | # conftest.py 3 | # 4 | # Copyright (c) 2016-2023 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | from random import random 22 | 23 | import pytest 24 | 25 | from fraudar import ReviewGraph 26 | 27 | 28 | @pytest.fixture 29 | def review_graph() -> ReviewGraph: 30 | """Returns a sample review graph. 31 | 32 | This function creates a graph and makes a sample graph defined as 33 | 34 | .. graphviz:: 35 | 36 | digraph bipartite { 37 | graph [rankdir = LR]; 38 | "reviewer-0"; 39 | "reviewer-1"; 40 | "product-0"; 41 | "product-1"; 42 | "product-2"; 43 | "reviewer-0" -> "product-0"; 44 | "reviewer-0" -> "product-1"; 45 | "reviewer-0" -> "product-2"; 46 | "reviewer-1" -> "product-1"; 47 | "reviewer-1" -> "product-2"; 48 | } 49 | 50 | """ 51 | g = ReviewGraph() 52 | for i in range(2): 53 | g.new_reviewer(f"reviewer-{i}") 54 | for j in range(3): 55 | g.new_product(f"product-{j}") 56 | for i, r in enumerate(g.reviewers): 57 | for p in g.products[i:]: 58 | g.add_review(r, p, random()) 59 | return g 60 | -------------------------------------------------------------------------------- /sample.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # 3 | # sample.py 4 | # 5 | # Copyright (c) 2016-2023 Junpei Kawamoto 6 | # 7 | # This file is part of rgmining-fraudar. 8 | # 9 | # rgmining-fraudar is free software: you can redistribute it and/or modify 10 | # it under the terms of the GNU General Public License as published by 11 | # the Free Software Foundation, either version 3 of the License, or 12 | # (at your option) any later version. 13 | # 14 | # rgmining-fraudar is distributed in the hope that it will be useful, 15 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | # GNU General Public License for more details. 18 | # 19 | # You should have received a copy of the GNU General Public License 20 | # along with rgmining-fraudar. If not, see . 21 | # 22 | """A simple sample script running FRAUDAR with a synthetic data set. 23 | 24 | This script requires `rgmining-synthetic-dataset`. 25 | To install that package, run 26 | 27 | .. code-block:: shell 28 | 29 | $ pip install --upgrade rgmining-synthetic-dataset 30 | 31 | """ 32 | 33 | import sys 34 | from os import path 35 | 36 | import click 37 | import synthetic 38 | 39 | sys.path.append(path.join(path.dirname(__file__), "../")) 40 | import fraudar # pylint: disable=import-error, wrong-import-position 41 | 42 | 43 | @click.command() 44 | @click.argument("blocks", type=int) 45 | def analyze(blocks: int) -> None: 46 | """Analyze a synthetic data set with a given number of blocks. 47 | 48 | Args: 49 | blocks: the number of blocks. 50 | """ 51 | graph = fraudar.ReviewGraph(blocks) 52 | synthetic.load(graph) 53 | 54 | graph.update() 55 | 56 | detected = [r for r in graph.reviewers if r.anomalous_score] 57 | correct = [r for r in detected if "anomaly" in r.name] 58 | 59 | print( 60 | len(correct) / len(detected), 61 | len(correct) / synthetic.ANOMALOUS_REVIEWER_SIZE, 62 | ) 63 | 64 | 65 | if __name__ == "__main__": 66 | analyze() # pylint: disable=no-value-for-parameter 67 | -------------------------------------------------------------------------------- /fraudar/__init__.py: -------------------------------------------------------------------------------- 1 | # 2 | # __init__.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | """A wrapper of Fraudar algorithm for the review graph mining project. 22 | 23 | The Fraudar has been introduced by Bryan Hooi, *et al.* in 24 | ACM SIGKDD 2016 Conference on Knowledge Discovery & Data Mining (KDD 2016). 25 | 26 | This package exports :class:`ReviewGraph ` class, 27 | which implements interfaces expected in other APIs of 28 | `Review Graph Mining project `_, 29 | and three sub algorithms used in FRAUDER: 30 | 31 | * :meth:`aveDegree ` computes average degree on a matrix, 32 | * :meth:`sqrtWeightedAveDegree ` 33 | computes square-weighted average degree on a matrix, 34 | * :meth:`logWeightedAveDegree ` 35 | computes logarithm-weighted average degree on a matrix. 36 | 37 | :meth:`ReviewGraph ` takes keyword argument ``algo`` to 38 | be set the sub algorithm to be used. 39 | """ 40 | 41 | from typing import Final 42 | 43 | from fraudar.export.greedy import ( 44 | aveDegree, 45 | logWeightedAveDegree, 46 | sqrtWeightedAveDegree, 47 | ) 48 | from fraudar.graph import ReviewGraph 49 | 50 | __all__: Final = ( 51 | "ReviewGraph", 52 | "aveDegree", 53 | "sqrtWeightedAveDegree", 54 | "logWeightedAveDegree", 55 | ) 56 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | build-backend = "poetry.core.masonry.api" 3 | 4 | requires = [ "poetry-core>=1" ] 5 | 6 | [project] 7 | name = "rgmining-fraudar" 8 | version = "0.7.6" 9 | description = "A wrapper of Fraudar algorithm for Review graph mining project" 10 | readme = "README.rst" 11 | keywords = [ "algorithm", "fraudar", "graph", "kdd", "mining", "review" ] 12 | license = "GPL-3.0-only" 13 | authors = [ 14 | { name = "Junpei Kawamoto", email = "kawamoto.junpei@gmail.com" }, 15 | ] 16 | requires-python = ">=3.11" 17 | classifiers = [ 18 | "Development Status :: 4 - Beta", 19 | "Intended Audience :: Science/Research", 20 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 21 | "Natural Language :: English", 22 | "Programming Language :: Python", 23 | "Programming Language :: Python :: 3 :: Only", 24 | "Programming Language :: Python :: 3.11", 25 | "Programming Language :: Python :: 3.12", 26 | "Programming Language :: Python :: 3.13", 27 | "Programming Language :: Python :: 3.14", 28 | "Topic :: Scientific/Engineering :: Information Analysis", 29 | "Topic :: Software Development :: Libraries", 30 | ] 31 | 32 | dependencies = [ 33 | "numpy>=2.2", 34 | "scikit-learn>=1.6.1", 35 | ] 36 | 37 | optional-dependencies.sample = [ 38 | "click>=8", 39 | "rgmining-synthetic-dataset>=0.9", 40 | ] 41 | urls.documentation = "https://rgmining.github.io/fraudar/" 42 | urls.homepage = "https://rgmining.github.io/fraudar/" 43 | urls.repository = "https://github.com/rgmining/fraudar" 44 | 45 | [tool.poetry] 46 | packages = [ 47 | { include = "fraudar" }, 48 | ] 49 | include = [ "COPYING", "LICENSE-2.0" ] 50 | 51 | [tool.poetry.group.dev.dependencies] 52 | bump-my-version = "^1.0.2" 53 | pre-commit = "^4.1" 54 | pre-commit-uv = "^4.1.4" 55 | pytest = "^8.3.5" 56 | mypy = "^1.15.0" 57 | pytest-cov = "^6.0.0" 58 | pytest-mock = "^3.14.0" 59 | ruff = "^0.11.0" 60 | 61 | [tool.poetry.group.docs] 62 | optional = true 63 | 64 | [tool.poetry.group.docs.dependencies] 65 | sphinx = "^8.2.3" 66 | sphinx-rtd-theme = "^3.0.2" 67 | sphinx-autobuild = "^2024.10.3" 68 | 69 | [tool.ruff] 70 | target-version = "py311" 71 | line-length = 79 72 | 73 | [tool.pytest.ini_options] 74 | addopts = "--cov=fraudar --cov-branch --cov-report=term-missing --cov-report=xml" 75 | 76 | [tool.coverage.run] 77 | omit = [ "fraudar/export/*" ] 78 | 79 | [tool.coverage.report] 80 | exclude_lines = [ "\\.{3}" ] 81 | 82 | [tool.bumpversion] 83 | current_version = "0.7.6" 84 | commit = true 85 | pre_commit_hooks = [ 86 | "poetry lock", 87 | "git add poetry.lock", 88 | ] 89 | 90 | [[tool.bumpversion.files]] 91 | filename = "pyproject.toml" 92 | search = 'version = "{current_version}"' 93 | replace = 'version = "{new_version}"' 94 | 95 | [[tool.bumpversion.files]] 96 | filename = "README.rst" 97 | 98 | [tool.mypy] 99 | files = "fraudar/*.py,tests/**/*.py" 100 | warn_return_any = true 101 | warn_unused_configs = true 102 | disallow_untyped_defs = true 103 | -------------------------------------------------------------------------------- /fraudar/export/MinTree.py: -------------------------------------------------------------------------------- 1 | # 2 | # MinTree.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | # This file was originally made by Bryan Hooi et al, 22 | # and distributed under Apache License, Version 2.0. 23 | # 24 | # mypy: ignore-errors 25 | import math 26 | 27 | 28 | class MinTree: 29 | """ 30 | A tree data structure which stores a list of degrees and can quickly retrieve the min degree element, 31 | or modify any of the degrees, each in logarithmic time. It works by creating a binary tree with the 32 | given elements in the leaves, where each internal node stores the min of its two children. 33 | """ 34 | 35 | def __init__(self, degrees): 36 | self.height = int(math.ceil(math.log(len(degrees), 2))) 37 | self.numLeaves = 2**self.height 38 | self.numBranches = self.numLeaves - 1 39 | self.n = self.numBranches + self.numLeaves 40 | self.nodes = [float("inf")] * self.n 41 | for i in range(len(degrees)): 42 | self.nodes[self.numBranches + i] = degrees[i] 43 | for i in reversed(range(self.numBranches)): 44 | self.nodes[i] = min(self.nodes[2 * i + 1], self.nodes[2 * i + 2]) 45 | 46 | # @profile 47 | def getMin(self): 48 | cur = 0 49 | for i in range(self.height): 50 | cur = ( 51 | (2 * cur + 1) 52 | if self.nodes[2 * cur + 1] <= self.nodes[2 * cur + 2] 53 | else (2 * cur + 2) 54 | ) 55 | # print "found min at %d: %d" % (cur, self.nodes[cur]) 56 | return (cur - self.numBranches, self.nodes[cur]) 57 | 58 | # @profile 59 | def changeVal(self, idx, delta): 60 | cur = self.numBranches + idx 61 | self.nodes[cur] += delta 62 | for i in range(self.height): 63 | cur = (cur - 1) // 2 64 | nextParent = min(self.nodes[2 * cur + 1], self.nodes[2 * cur + 2]) 65 | if self.nodes[cur] == nextParent: 66 | break 67 | self.nodes[cur] = nextParent 68 | 69 | def dump(self): 70 | print( 71 | f"numLeaves: {self.numLeaves}, numBranches: {self.numBranches}, n: {self.n}, nodes: " 72 | ) 73 | cur = 0 74 | for i in range(self.height + 1): 75 | for j in range(2**i): 76 | print(self.nodes[cur], end=" ") 77 | cur += 1 78 | print("") 79 | -------------------------------------------------------------------------------- /tests/test_review_graph.py: -------------------------------------------------------------------------------- 1 | # 2 | # test_review_graph.py 3 | # 4 | # Copyright (c) 2016-2023 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | from collections import defaultdict 22 | from io import StringIO 23 | from random import random 24 | 25 | import pytest 26 | 27 | from fraudar import ReviewGraph 28 | from fraudar.graph import Product, Reviewer 29 | 30 | 31 | def test_new_reviewer(review_graph: ReviewGraph) -> None: 32 | """Test new_reviewer method.""" 33 | name = "test-reviewer" 34 | reviewer = review_graph.new_reviewer(name) 35 | assert reviewer.name == name 36 | assert reviewer in review_graph.reviewers 37 | 38 | 39 | def test_new_product(review_graph: ReviewGraph) -> None: 40 | """Test new_product method.""" 41 | name = "test-product" 42 | product = review_graph.new_product(name) 43 | assert product.name == name 44 | assert product in review_graph.products 45 | 46 | 47 | def test_add_review() -> None: 48 | """Test add_review method.""" 49 | graph = ReviewGraph() 50 | reviewers = [graph.new_reviewer(f"reviewer-{i}") for i in range(3)] 51 | products = [graph.new_product(f"product-{i}") for i in range(3)] 52 | reviews = defaultdict[Reviewer, dict[Product, float]](dict) 53 | 54 | for r in reviewers: 55 | for p in products: 56 | rating = random() 57 | reviews[r][p] = graph.add_review(r, p, rating) 58 | assert reviews[r][p] == rating 59 | for r in reviewers: 60 | for p in products: 61 | assert graph.reviews[p][r] == reviews[r][p] 62 | 63 | 64 | def test_store_matrix() -> None: 65 | """Test store matrix method. 66 | 67 | This function creates a graph and makes a sample graph defiend as 68 | 69 | .. graphviz:: 70 | 71 | digraph bipartite { 72 | graph [rankdir = LR]; 73 | "reviewer-0"; 74 | "reviewer-1"; 75 | "product-0"; 76 | "product-1"; 77 | "product-2"; 78 | "reviewer-0" -> "product-0"; 79 | "reviewer-0" -> "product-1"; 80 | "reviewer-0" -> "product-2"; 81 | "reviewer-1" -> "product-1"; 82 | "reviewer-1" -> "product-2"; 83 | } 84 | 85 | """ 86 | graph = ReviewGraph() 87 | reviewers = [graph.new_reviewer(f"reviewer-{i}") for i in range(2)] 88 | products = [graph.new_product(f"product-{i}") for i in range(3)] 89 | reviews = defaultdict[Reviewer, dict[Product, bool]](dict) 90 | for i, r in enumerate(reviewers): 91 | for p in products[i:]: 92 | graph.add_review(r, p, random()) 93 | reviews[r][p] = True 94 | 95 | buf = StringIO() 96 | graph._store_matrix(buf) # pylint: disable=protected-access 97 | 98 | found = False 99 | for line in buf.getvalue().split("\n"): 100 | if not line: 101 | break 102 | i, j = [int(s.strip()) for s in line.split(" ")] 103 | r = graph.reviewers[i] 104 | p = graph.products[j] 105 | assert r in reviews 106 | assert p in reviews[r] 107 | found = True 108 | 109 | if not found: 110 | pytest.fail("_store_matrix returns empty file.") 111 | 112 | 113 | def test_update(review_graph: ReviewGraph) -> None: 114 | """Test update method doesn't raise any errors.""" 115 | review_graph.update() 116 | -------------------------------------------------------------------------------- /fraudar/export/README: -------------------------------------------------------------------------------- 1 | FRAUDAR: Bounding Graph Fraud in the Face of Camouflage 2 | Authors: Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos 3 | 4 | This software is licensed under Apache License, Version 2.0 (the "License"); 5 | you may not use this file except in compliance with the License. 6 | You may obtain a copy of the License at 7 | 8 | http://www.apache.org/licenses/LICENSE-2.0 9 | 10 | Unless required by applicable law or agreed to in writing, software 11 | distributed under the License is distributed on an "AS IS" BASIS, 12 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | See the License for the specific language governing permissions and 14 | limitations under the License. 15 | 16 | Version: 1.1 17 | Date: June 12, 2018 18 | Main Contact: Bryan Hooi (bhooi@andrew.cmu.edu) 19 | 20 | 21 | FRAUDAR is designed to catch fraudulent blocks in graph datasets (e.g. a review graph, Twitter follow graph, etc.) in a camouflage-resistant way. 22 | 23 | It detects blocks that are unusually dense while having low column degree. 24 | 25 | ============ RUNNING FRAUDAR ON A TEST EXAMPLE ============ 26 | 27 | The simplest way to run FRAUDAR is to use run_greedy.py, e.g.: 28 | 29 | python run_greedy.py data/example.txt out/out 30 | 31 | This requires the python dependencies numpy and scipy (which can either be installed on their own or through a bundle like Anaconda) 32 | 33 | The above command runs the greedy algorithm without node suspiciousness values on an example dataset (a sparse dataset with an injected 20 x 20 clique in the first 20 indices). As a result, the greedy algorithm should detect this 20 x 20 block. 34 | 35 | The first argument (data/example.csv) is the data file to use, while the second (out/out) is the location and filename to save the results. The output will be saved as "out/out.rows" and "out/out.cols", which are the set of rows and columns respectively in the block. 36 | 37 | *Optional*: If you want to include node suspiciousness values (i.e. obtained from some external source of information about how suspicious each node is), you can also add their path as a third argument: i.e. 38 | 39 | python run_greedy.py data/example.txt out/out data/susp 40 | 41 | In this case the suspiciousness values (one value for each row and column) are stored as "data/susp.rows" and "data/susp.cols". 42 | 43 | A public amazon dataset, arts.csv used in the paper can be obtained from https://snap.stanford.edu/data/. The public twitter dataset used in the paper can be obtained from http://an.kaist.ac.kr/traces/WWW2010.html. 44 | 45 | ============ FUNCTIONS IN GREEDY.PY ============ 46 | 47 | The main functions available for use in greedy.py are: 48 | 49 | * readData: takes in the path to a file, which we read and then convert to a python sparse matrix. The input file should be a whitespace separated file of the form: 50 | 51 | (row_index) (col_index) (optional: 1) 52 | 53 | (the "1" refers an edge weight of 1, but will be ignored since we assume an unweighted graph) 54 | 55 | * aveDegree, sqrtWeightedAveDegree, and logWeightedAveDegree. For all of them, their first argument is the python sparse matrix of the form returned by readData. Their second *optional* argument is either None or a tuple (row_susp, col_susp), the node suspiciousness score vectors, which have length equal to the number of rows and columns respectively. We recommend the default of None (or not passing any 2nd argument), meaning no node suspiciousness, unless you have a clear idea of some external suspiciousness values to use. See run_greedy.py for an example. One possible use case is if the default is returning too large blocks and you want smaller and denser blocks, you can pass in constant positive vectors as node suspiciousness, which will tend to give smaller blocks. 56 | 57 | All these functions return a tuple ((row_indices, col_indices), score), i.e. the row and column indices of the detected block as well as the score value it receives. 58 | 59 | These functions only differ in the weighting scheme: aveDegree is unweighted (all edges have weight 1); sqrtWeightedAveDegree weights each edge by 1/sqrt(column degree) (i.e. column sum), while logWeightedAveDegree weights each weight by 1/log(column degree). 60 | 61 | There are also some additional helper functions: 62 | 63 | * subsetAboveDegree: remove all edges below a certain degree 64 | 65 | * detectMultiple: for detecting multiple blocks 66 | 67 | * shuffleMatrix: shuffle matrix rows and cols randomly 68 | 69 | * injectCliqueCamo: inject a clique and various types of camouflage 70 | 71 | * jaccard/getPrecision/getRecall/etc: various measures of the accuracy of the detected (row_indices, col_indices) tuple with respect to some ground truth tuple. 72 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ### Generated by gibo (https://github.com/simonwhitaker/gibo) 2 | ### https://raw.github.com/github/gitignore/fc6ce5da28a8c3480cc8a5acad050449f72a9261/Python.gitignore 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[codz] 7 | *$py.class 8 | 9 | # C extensions 10 | *.so 11 | 12 | # Distribution / packaging 13 | .Python 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | wheels/ 26 | share/python-wheels/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .nox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | *.py.cover 53 | .hypothesis/ 54 | .pytest_cache/ 55 | cover/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | db.sqlite3-journal 66 | 67 | # Flask stuff: 68 | instance/ 69 | .webassets-cache 70 | 71 | # Scrapy stuff: 72 | .scrapy 73 | 74 | # Sphinx documentation 75 | docs/_build/ 76 | 77 | # PyBuilder 78 | .pybuilder/ 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | # For a library or package, you might want to ignore these files since the code is 90 | # intended to run in multiple environments; otherwise, check them in: 91 | # .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | # Pipfile.lock 99 | 100 | # UV 101 | # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. 102 | # This is especially recommended for binary packages to ensure reproducibility, and is more 103 | # commonly ignored for libraries. 104 | # uv.lock 105 | 106 | # poetry 107 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 108 | # This is especially recommended for binary packages to ensure reproducibility, and is more 109 | # commonly ignored for libraries. 110 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 111 | poetry.lock 112 | poetry.toml 113 | 114 | # pdm 115 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 116 | # pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. 117 | # https://pdm-project.org/en/latest/usage/project/#working-with-version-control 118 | # pdm.lock 119 | # pdm.toml 120 | .pdm-python 121 | .pdm-build/ 122 | 123 | # pixi 124 | # Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. 125 | # pixi.lock 126 | # Pixi creates a virtual environment in the .pixi directory, just like venv module creates one 127 | # in the .venv directory. It is recommended not to include this directory in version control. 128 | .pixi 129 | 130 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 131 | __pypackages__/ 132 | 133 | # Celery stuff 134 | celerybeat-schedule 135 | celerybeat.pid 136 | 137 | # Redis 138 | *.rdb 139 | *.aof 140 | *.pid 141 | 142 | # RabbitMQ 143 | mnesia/ 144 | rabbitmq/ 145 | rabbitmq-data/ 146 | 147 | # ActiveMQ 148 | activemq-data/ 149 | 150 | # SageMath parsed files 151 | *.sage.py 152 | 153 | # Environments 154 | .env 155 | .envrc 156 | .venv 157 | env/ 158 | venv/ 159 | ENV/ 160 | env.bak/ 161 | venv.bak/ 162 | 163 | # Spyder project settings 164 | .spyderproject 165 | .spyproject 166 | 167 | # Rope project settings 168 | .ropeproject 169 | 170 | # mkdocs documentation 171 | /site 172 | 173 | # mypy 174 | .mypy_cache/ 175 | .dmypy.json 176 | dmypy.json 177 | 178 | # Pyre type checker 179 | .pyre/ 180 | 181 | # pytype static type analyzer 182 | .pytype/ 183 | 184 | # Cython debug symbols 185 | cython_debug/ 186 | 187 | # PyCharm 188 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 189 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 190 | # and can be added to the global gitignore or merged into this file. For a more nuclear 191 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 192 | # .idea/ 193 | 194 | # Abstra 195 | # Abstra is an AI-powered process automation framework. 196 | # Ignore directories containing user credentials, local state, and settings. 197 | # Learn more at https://abstra.io/docs 198 | .abstra/ 199 | 200 | # Visual Studio Code 201 | # Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 202 | # that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore 203 | # and can be added to the global gitignore or merged into this file. However, if you prefer, 204 | # you could uncomment the following to ignore the entire vscode folder 205 | # .vscode/ 206 | 207 | # Ruff stuff: 208 | .ruff_cache/ 209 | 210 | # PyPI configuration file 211 | .pypirc 212 | 213 | # Marimo 214 | marimo/_static/ 215 | marimo/_lsp/ 216 | __marimo__/ 217 | 218 | # Streamlit 219 | .streamlit/secrets.toml 220 | -------------------------------------------------------------------------------- /fraudar/graph.py: -------------------------------------------------------------------------------- 1 | # 2 | # graph.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | """Provide a review graph which runs Fraudar algorithm.""" 22 | 23 | import tempfile 24 | from bisect import bisect_left 25 | from collections import defaultdict 26 | from typing import Any, Final, Protocol 27 | 28 | import numpy as np 29 | 30 | from fraudar.export import greedy 31 | from fraudar.export.greedy import logWeightedAveDegree 32 | 33 | 34 | class Node: 35 | """Node of the ReviewGraph. 36 | 37 | A node has a name and a link to the graph. It also implements 38 | :meth:`__hash__` function so that each node can be stored in 39 | dictionaries. 40 | 41 | Args: 42 | graph: graph object this node belongs to. 43 | name: name of this node. 44 | """ 45 | 46 | graph: Final["ReviewGraph"] 47 | """The graph object this node belongs to.""" 48 | name: Final[str] 49 | """Name of this node.""" 50 | 51 | __slots__ = ("graph", "name") 52 | 53 | def __init__(self, graph: "ReviewGraph", name: str) -> None: 54 | """Construct a node instance. 55 | 56 | Args: 57 | graph: graph object this node belongs to. 58 | name: name of this node. 59 | """ 60 | self.graph = graph 61 | self.name = name 62 | 63 | def __hash__(self) -> int: 64 | """Returns a hash value of this instance.""" 65 | return 13 * hash(type(self)) + 17 * hash(self.name) 66 | 67 | def __lt__(self, other: "Node") -> bool: 68 | return self.name.__lt__(other.name) 69 | 70 | 71 | class Reviewer(Node): 72 | """A node type representing a reviewer. 73 | 74 | Use :meth:`ReviewGraph.new_reviewer` to create a new reviewer object 75 | instead of using this constructor directory. 76 | 77 | Args: 78 | graph: graph object this reviewer belongs to. 79 | name: name of this reviewer. 80 | """ 81 | 82 | anomalous_score: float 83 | """anomalous score of this reviewer.""" 84 | 85 | __slots__ = ("anomalous_score",) 86 | 87 | def __init__( 88 | self, graph: "ReviewGraph", name: str, anomalous_score: float = 0 89 | ) -> None: 90 | super().__init__(graph, name) 91 | self.anomalous_score = anomalous_score 92 | 93 | 94 | class Product(Node): 95 | """A node type representing a product. 96 | 97 | Use :meth:`ReviewGraph.new_product` to create a new product object 98 | instead of using this constructor directory. 99 | 100 | Args: 101 | graph: graph object this product belongs to. 102 | name: name of this product. 103 | """ 104 | 105 | __slots__ = () 106 | 107 | @property 108 | def summary(self) -> float: 109 | """Summary of ratings given to this product.""" 110 | reviewers = self.graph.reviews[self].keys() 111 | ratings = [self.graph.reviews[self][r] for r in reviewers] 112 | weights = [1 - r.anomalous_score for r in reviewers] 113 | if sum(weights) == 0: 114 | return float(np.mean(ratings)) 115 | else: 116 | return float(np.average(ratings, weights=weights)) 117 | 118 | 119 | class _Writable(Protocol): 120 | def write(self, s: str, /) -> int: ... 121 | 122 | 123 | class ReviewGraph: 124 | """ReviewGraph is a simple bipartite graph representing review relation. 125 | 126 | Args: 127 | blocks: how many blocks to be detected. (default: 1) 128 | algo: algorithm used in fraudar, chosen from 129 | :meth:`aveDegree `, 130 | :meth:`sqrtWeightedAveDegree `, 131 | and 132 | :meth:`logWeightedAveDegree `. 133 | (default: logWeightedAveDegree) 134 | """ 135 | 136 | reviewers: Final[list[Reviewer]] 137 | """Collection of reviewers.""" 138 | products: Final[list[Product]] 139 | """Collection of products.""" 140 | reviews: Final[defaultdict[Product, dict[Reviewer, float]]] 141 | """Collection of reviews. 142 | 143 | reviews is a dictionary of which key is a product and value is another 144 | dictionary of which key is a reviewer and value is a rating from the 145 | reviewer to the product. 146 | """ 147 | _algo: Final[Any] 148 | _blocks: Final[int] 149 | 150 | def __init__( 151 | self, blocks: int = 1, algo: Any = logWeightedAveDegree 152 | ) -> None: 153 | self.reviewers = [] 154 | self.products = [] 155 | self.reviews = defaultdict(dict) 156 | 157 | self._algo = algo 158 | self._blocks = blocks 159 | 160 | def new_reviewer( 161 | self, name: str, anomalous_score: float | None = None 162 | ) -> Reviewer: 163 | """Create a new reviewer. 164 | 165 | Args: 166 | name: name of the new reviewer. 167 | 168 | Returns: 169 | a new reviewer object. 170 | """ 171 | r = Reviewer(self, name, anomalous_score or 0) 172 | self.reviewers.append(r) 173 | return r 174 | 175 | def new_product(self, name: str) -> Product: 176 | """Create a new product. 177 | 178 | Args: 179 | name: name of the new product. 180 | 181 | Returns: 182 | a new product object. 183 | """ 184 | p = Product(self, name) 185 | self.products.append(p) 186 | return p 187 | 188 | def add_review( 189 | self, 190 | reviewer: Reviewer, 191 | product: Product, 192 | rating: float, 193 | _time: int | None = None, 194 | ) -> float: 195 | """Add a review from a reviewer to a product. 196 | 197 | Args: 198 | reviewer: reviewer who posts the review. 199 | product: product which receives the review. 200 | rating: the review score. 201 | 202 | Returns: 203 | added review score. 204 | """ 205 | self.reviews[product][reviewer] = rating 206 | return rating 207 | 208 | def update(self) -> float: 209 | """Update anomalous scores by running a greedy algorithm. 210 | 211 | Returns: 212 | 0 213 | """ 214 | with tempfile.NamedTemporaryFile(mode="w") as fp: 215 | # Store this graph to a temporal file. 216 | self._store_matrix(fp) 217 | fp.seek(0) 218 | 219 | # Run greedy algorithm. 220 | M = greedy.readData(fp.name) 221 | res = greedy.detectMultiple(M, self._algo, self._blocks) 222 | 223 | # Update anomalous scores. 224 | for block in res: 225 | for i in block[0][0]: 226 | self.reviewers[i].anomalous_score = 1 227 | 228 | return 0 229 | 230 | def _store_matrix(self, fp: _Writable) -> None: 231 | """Store this graph as a sparse matrix format. 232 | 233 | Args: 234 | fp: file-like object where the matrix to be written. 235 | """ 236 | self.reviewers.sort() 237 | self.products.sort() 238 | for p in self.reviews: 239 | j = bisect_left(self.products, p) 240 | for r in self.reviews[p]: 241 | i = bisect_left(self.reviewers, r) 242 | fp.write("{0} {1}\n".format(i, j)) 243 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = poetry run sphinx-build 7 | SPHINXAPIDOC = poetry run sphinx-apidoc 8 | PAPER = 9 | BUILDDIR = build 10 | 11 | # Internal variables. 12 | PAPEROPT_a4 = -D latex_paper_size=a4 13 | PAPEROPT_letter = -D latex_paper_size=letter 14 | ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 15 | # the i18n builder cannot share the environment and doctrees with the others 16 | I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 17 | 18 | .PHONY: help 19 | help: 20 | @echo "Please use \`make ' where is one of" 21 | @echo " html to make standalone HTML files" 22 | @echo " dirhtml to make HTML files named index.html in directories" 23 | @echo " singlehtml to make a single large HTML file" 24 | @echo " pickle to make pickle files" 25 | @echo " json to make JSON files" 26 | @echo " htmlhelp to make HTML files and a HTML help project" 27 | @echo " qthelp to make HTML files and a qthelp project" 28 | @echo " applehelp to make an Apple Help Book" 29 | @echo " devhelp to make HTML files and a Devhelp project" 30 | @echo " epub to make an epub" 31 | @echo " epub3 to make an epub3" 32 | @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" 33 | @echo " latexpdf to make LaTeX files and run them through pdflatex" 34 | @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" 35 | @echo " text to make text files" 36 | @echo " man to make manual pages" 37 | @echo " texinfo to make Texinfo files" 38 | @echo " info to make Texinfo files and run them through makeinfo" 39 | @echo " gettext to make PO message catalogs" 40 | @echo " changes to make an overview of all changed/added/deprecated items" 41 | @echo " xml to make Docutils-native XML files" 42 | @echo " pseudoxml to make pseudoxml-XML files for display purposes" 43 | @echo " linkcheck to check all external links for integrity" 44 | @echo " doctest to run all doctests embedded in the documentation (if enabled)" 45 | @echo " coverage to run coverage check of the documentation (if enabled)" 46 | @echo " dummy to check syntax errors of document sources" 47 | 48 | .PHONY: clean 49 | clean: 50 | rm -rf $(BUILDDIR)/* 51 | 52 | .PHONY: sphinx-apidoc 53 | sphinx-apidoc: 54 | $(SPHINXAPIDOC) -f -o source/modules -M "../fraudar/" 55 | rm source/modules/modules.rst 56 | 57 | .PHONY: html 58 | html: 59 | $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html 60 | @echo 61 | @echo "Build finished. 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Dummy builder generates no files." 232 | -------------------------------------------------------------------------------- /docs/source/conf.py: -------------------------------------------------------------------------------- 1 | # mypy: ignore-errors 2 | # 3 | # This file is execfile()d with the current directory set to its 4 | # containing dir. 5 | # 6 | # Note that not all possible configuration values are present in this 7 | # autogenerated file. 8 | # 9 | # All configuration values have a default; values that are commented out 10 | # serve to show the default. 11 | from datetime import datetime 12 | from importlib.metadata import version as get_version 13 | 14 | # noinspection PyUnresolvedReferences 15 | 16 | 17 | # If extensions (or modules to document with autodoc) are in another directory, 18 | # add these directories to sys.path here. If the directory is relative to the 19 | # documentation root, use os.path.abspath to make it absolute, like shown here. 20 | # 21 | 22 | # -- General configuration ------------------------------------------------ 23 | 24 | # If your documentation needs a minimal Sphinx version, state it here. 25 | # 26 | # needs_sphinx = '1.0' 27 | 28 | # Add any Sphinx extension module names here, as strings. They can be 29 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 30 | # ones. 31 | extensions = [ 32 | "sphinx.ext.autodoc", 33 | "sphinx.ext.intersphinx", 34 | "sphinx.ext.mathjax", 35 | "sphinx.ext.viewcode", 36 | "sphinx.ext.githubpages", 37 | "sphinx.ext.graphviz", 38 | "sphinx.ext.napoleon", 39 | "sphinx_rtd_theme", 40 | ] 41 | 42 | # Add any paths that contain templates here, relative to this directory. 43 | templates_path = ["_templates"] 44 | 45 | # The suffix(es) of source filenames. 46 | # You can specify multiple suffix as a list of string: 47 | # 48 | # source_suffix = ['.rst', '.md'] 49 | source_suffix = ".rst" 50 | 51 | # The encoding of source files. 52 | # 53 | # source_encoding = 'utf-8-sig' 54 | 55 | # The master toctree document. 56 | master_doc = "index" 57 | 58 | # General information about the project. 59 | project = "Wrapper of FRAUDAR algorithm" 60 | copyright = f"2016-{datetime.now().year}, Junpei Kawamoto" 61 | author = "Junpei Kawamoto" 62 | 63 | # The version info for the project you're documenting, acts as replacement for 64 | # |version| and |release|, also used in various other places throughout the 65 | # built documents. 66 | # 67 | # The full version, including alpha/beta/rc tags. 68 | release = get_version("rgmining-fraudar") 69 | # The short X.Y version. 70 | version = release[:3] 71 | 72 | # The language for content autogenerated by Sphinx. Refer to documentation 73 | # for a list of supported languages. 74 | # 75 | # This is also used if you do content translation via gettext catalogs. 76 | # Usually you set "language" from the command line for these cases. 77 | language = "en" 78 | 79 | # There are two options for replacing |today|: either, you set today to some 80 | # non-false value, then it is used: 81 | # 82 | # today = '' 83 | # 84 | # Else, today_fmt is used as the format for a strftime call. 85 | # 86 | # today_fmt = '%B %d, %Y' 87 | 88 | # List of patterns, relative to source directory, that match files and 89 | # directories to ignore when looking for source files. 90 | # This patterns also effect to html_static_path and html_extra_path 91 | exclude_patterns = [] 92 | 93 | # The reST default role (used for this markup: `text`) to use for all 94 | # documents. 95 | # 96 | # default_role = None 97 | 98 | # If true, '()' will be appended to :func: etc. cross-reference text. 99 | # 100 | # add_function_parentheses = True 101 | 102 | # If true, the current module name will be prepended to all description 103 | # unit titles (such as .. function::). 104 | # 105 | # add_module_names = True 106 | 107 | # If true, sectionauthor and moduleauthor directives will be shown in the 108 | # output. They are ignored by default. 109 | # 110 | # show_authors = False 111 | 112 | # The name of the Pygments (syntax highlighting) style to use. 113 | pygments_style = "sphinx" 114 | 115 | # A list of ignored prefixes for module index sorting. 116 | # modindex_common_prefix = [] 117 | 118 | # If true, keep warnings as "system message" paragraphs in the built documents. 119 | # keep_warnings = False 120 | 121 | # If true, `todo` and `todoList` produce output, else they produce nothing. 122 | todo_include_todos = False 123 | 124 | # To add GitHub link. 125 | rst_prolog = """ 126 | :github_url: https://github.com/rgmining/fraudar 127 | """ 128 | 129 | # -- Options for HTML output ---------------------------------------------- 130 | 131 | # The theme to use for HTML and HTML Help pages. See the documentation for 132 | # a list of builtin themes. 133 | # 134 | html_theme = "sphinx_rtd_theme" 135 | 136 | html_context = { 137 | "base_url": "https://rgmining.github.io/fraudar/", 138 | "twitter": "@junkawamoto", 139 | "google_analytics": "UA-82315630-2", 140 | "google_adsense": "ca-pub-4734862314145555", 141 | "google_adsense_slot": "6934003829", 142 | "addthis": "ra-57f1fa5b740feddd", 143 | } 144 | 145 | # Theme options are theme-specific and customize the look and feel of a theme 146 | # further. For a list of options available for each theme, see the 147 | # documentation. 148 | # 149 | html_theme_options = {"collapse_navigation": True} 150 | # Add any paths that contain custom themes here, relative to this directory. 151 | # html_theme_path = [] 152 | 153 | # The name for this set of Sphinx documents. 154 | # " v documentation" by default. 155 | # 156 | # html_title = u'' 157 | 158 | # A shorter title for the navigation bar. Default is the same as html_title. 159 | # 160 | # html_short_title = None 161 | 162 | # The name of an image file (relative to this directory) to place at the top 163 | # of the sidebar. 164 | # 165 | # html_logo = None 166 | 167 | # The name of an image file (relative to this directory) to use as a favicon of 168 | # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 169 | # pixels large. 170 | # 171 | # html_favicon = None 172 | 173 | # Add any paths that contain custom static files (such as style sheets) here, 174 | # relative to this directory. They are copied after the builtin static files, 175 | # so a file named "default.css" will overwrite the builtin "default.css". 176 | html_static_path = ["_static"] 177 | 178 | # Add any extra paths that contain custom files (such as robots.txt or 179 | # .htaccess) here, relative to this directory. These files are copied 180 | # directly to the root of the documentation. 181 | # 182 | # html_extra_path = [] 183 | 184 | # If not None, a 'Last updated on:' timestamp is inserted at every page 185 | # bottom, using the given strftime format. 186 | # The empty string is equivalent to '%b %d, %Y'. 187 | # 188 | # html_last_updated_fmt = None 189 | 190 | # If true, SmartyPants will be used to convert quotes and dashes to 191 | # typographically correct entities. 192 | # 193 | # html_use_smartypants = True 194 | 195 | # Custom sidebar templates, maps document names to template names. 196 | # 197 | # html_sidebars = {} 198 | 199 | # Additional templates that should be rendered to pages, maps page names to 200 | # template names. 201 | # 202 | # html_additional_pages = {} 203 | 204 | # If false, no module index is generated. 205 | # 206 | # html_domain_indices = True 207 | 208 | # If false, no index is generated. 209 | # 210 | # html_use_index = True 211 | 212 | # If true, the index is split into individual pages for each letter. 213 | # 214 | # html_split_index = False 215 | 216 | # If true, links to the reST sources are added to the pages. 217 | # 218 | # html_show_sourcelink = True 219 | 220 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 221 | # 222 | # html_show_sphinx = True 223 | 224 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 225 | # 226 | # html_show_copyright = True 227 | 228 | # If true, an OpenSearch description file will be output, and all pages will 229 | # contain a tag referring to it. The value of this option must be the 230 | # base URL from which the finished HTML is served. 231 | # 232 | html_use_opensearch = "https://docs.python.org" 233 | 234 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 235 | # html_file_suffix = None 236 | 237 | # Language to be used for generating the HTML full-text search index. 238 | # Sphinx supports the following languages: 239 | # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' 240 | # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' 241 | # 242 | # html_search_language = 'en' 243 | 244 | # A dictionary with options for the search language support, empty by default. 245 | # 'ja' uses this config value. 246 | # 'zh' user can custom change `jieba` dictionary path. 247 | # 248 | # html_search_options = {'type': 'default'} 249 | 250 | # The name of a javascript file (relative to the configuration directory) that 251 | # implements a search results scorer. If empty, the default will be used. 252 | # 253 | # html_search_scorer = 'scorer.js' 254 | 255 | # Output file base name for HTML help builder. 256 | htmlhelp_basename = "Fraudardoc" 257 | -------------------------------------------------------------------------------- /docs/source/index.rst: -------------------------------------------------------------------------------- 1 | :description: This package implements a wrapper of FRAUDAR algorithm to provide 2 | APIs defined in `Review Graph Mining project`_. 3 | 4 | .. _top: 5 | 6 | A wrapper of FRAUDAR algorithm 7 | ================================= 8 | .. raw:: html 9 | 10 |
11 | 12 | This package implements a wrapper of 13 | `FRAUDAR `_ 14 | algorithm to provide APIs defined in 15 | `Review Graph Mining project`_. 16 | 17 | 18 | Installation 19 | -------------- 20 | Use `pip` to install this package. 21 | 22 | .. code-block:: bash 23 | 24 | pip install --upgrade rgmining-fraudar 25 | 26 | 27 | .. _graph_model: 28 | 29 | Graph model 30 | ------------ 31 | FRAUDAR algorithm assumes review data are represented by a bipartite graph. 32 | This graph has two kinds of nodes, reviewers and products. 33 | A reviewer node and a product node are tied by an edge if the reviewer reviews 34 | the product. 35 | We extend the bipartite graph so that we can compute summary of rating scores. 36 | In our bipartite graph, each review, i.e. edge, has a normalized rating score, 37 | which means all scores are in :math:`[0, 1]`. 38 | 39 | .. graphviz:: 40 | 41 | digraph bipartite { 42 | graph [label="Bipartite graph example.", rankdir = LR]; 43 | "reviewer-0"; 44 | "reviewer-1"; 45 | "product-0"; 46 | "product-1"; 47 | "product-2"; 48 | "reviewer-0" -> "product-0" [label="0.2"]; 49 | "reviewer-0" -> "product-1" [label="0.9"]; 50 | "reviewer-0" -> "product-2" [label="0.6"]; 51 | "reviewer-1" -> "product-1" [label="0.1"]; 52 | "reviewer-1" -> "product-2" [label="0.7"]; 53 | } 54 | 55 | In the above bipartite graph example, there are two reviewers and three products. 56 | Both reviewers review product 1 and product 2, but product 0 is only reviewed by 57 | reviewer 0. 58 | 59 | 60 | Usage 61 | ------- 62 | 63 | Graph Construction 64 | ^^^^^^^^^^^^^^^^^^^ 65 | This package provides a review graph class 66 | :class:`fraudar.ReviewGraph ` 67 | which represents the above bipartite graph. 68 | The constructor of this class takes two arguments: 69 | the number of kinds of fraudulent patterns this algorithm assumes, 70 | and a type of subroutine. 71 | Currently, you can pick one from the following three functions as the type of 72 | subroutine: 73 | 74 | * :meth:`aveDegree ` (default), 75 | * :meth:`sqrtWeightedAveDegree `, 76 | * :meth:`logWeightedAveDegree `. 77 | 78 | See :doc:`API references ` and 79 | `the original article `_ 80 | for more information about the subroutines. 81 | 82 | To construct a review graph instance with assuming :math:`n` kinds of fraudulent 83 | patterns and using :meth:`aveDegree ` as the 84 | subroutine, 85 | 86 | .. code-block:: python 87 | 88 | import fraudar 89 | graph = fraudar.ReviewGraph(n, fraudar.aveDegree) 90 | 91 | The constructed graph object implements the 92 | :ref:`graph interface `. 93 | 94 | After constructing a graph instance, you need to add reviewer nodes, product 95 | nodes, and review edges. 96 | Two methods, :meth:`new_reviewer() ` and 97 | :meth:`new_product() `, 98 | create a reviewer node and a product node, respectively. 99 | Both methods take one argument `name` i.e. ID of the node. 100 | This name must be unique in a graph. 101 | 102 | Method :meth:`add_review() ` adds a review 103 | to the graph. 104 | It takes a reviewer object, a product object, and a rating score. 105 | The reviewer object and the product object must be created by the above two 106 | methods, and the rating score takes a float value in :math:`[0, 1]`. 107 | 108 | For example, let us construct a review graph instance which represents the 109 | bipartite graph example in the :ref:`Graph Model section`. 110 | The graph construction code is 111 | 112 | .. code-block:: python 113 | 114 | import fraudar 115 | 116 | # Construct a Review Graph instance. 117 | # In this example, we choose 1 as the `n`. 118 | n = 1 119 | graph = fraudar.ReviewGraph(n, fraudar.aveDegree) 120 | 121 | # Create reviewers and products. 122 | reviewers = [graph.new_reviewer("reviewer-{0}".format(i)) for i in range(2)] 123 | products = [graph.new_product("product-{0}".format(i)) for i in range(3)] 124 | 125 | # Add reviews. 126 | graph.add_review(reviewers[0], products[0], 0.2) 127 | graph.add_review(reviewers[0], products[1], 0.9) 128 | graph.add_review(reviewers[0], products[2], 0.6) 129 | graph.add_review(reviewers[1], products[0], 0.1) 130 | graph.add_review(reviewers[1], products[1], 0.7) 131 | 132 | 133 | Analysis 134 | ^^^^^^^^^ 135 | Method :meth:`update()` starts the 136 | FRAUDAR algorithm. 137 | 138 | .. code-block:: python 139 | 140 | # Run one iteration. 141 | graph.update() 142 | 143 | 144 | Result 145 | ^^^^^^^^ 146 | Each reviewer has an anomalous score. 147 | If the anomalous score of a reviewer is 1, the reviewer is classified 148 | in `FRAUD`, otherwise `HONEST`. 149 | Property :meth:`anomalous_score ` 150 | returns the anomalous score. 151 | 152 | The :class:`ReviewGraph ` has 153 | property :meth:`reviewers`, 154 | which returns a collection of reviewers, 155 | and you can list up `FRAUD` reviewer names by 156 | 157 | .. code-block:: python 158 | 159 | for r in graph.reviewers: 160 | if r.anomalous_score == 1: 161 | print(r.name) 162 | 163 | On the other hand, each product has a summarized rating score. 164 | The summarized rating score of a product is the average of rating scores posted 165 | to the product from `HONEST` reviewers. 166 | Property :meth:`summary` returns the 167 | summarized rating score. 168 | 169 | The :class:`ReviewGraph` also has 170 | property :meth:`products`, 171 | which returns a collection of products, 172 | and you can print summarized rating scores of all products by 173 | 174 | .. code-block:: python 175 | 176 | for p in graph.products: 177 | print(p.name, p.summary) 178 | 179 | 180 | Script 181 | ^^^^^^^ 182 | As the summary of the above usage, we make an executable script which takes 183 | the parameter ``n`` as a command line option, and analyze the above graph. 184 | Let us save the following script as ``analyze.py``. 185 | 186 | .. code-block:: python 187 | 188 | #!/usr/bin/env python 189 | import click 190 | import fraudar 191 | 192 | @click.command() 193 | @click.argument("n", type=int) 194 | def analyze(n): 195 | graph = fraudar.ReviewGraph(n, fraudar.aveDegree) 196 | 197 | # Create reviewers and products. 198 | reviewers = [graph.new_reviewer("reviewer-{0}".format(i)) for i in range(2)] 199 | products = [graph.new_product("product-{0}".format(i)) for i in range(3)] 200 | 201 | # Add reviews. 202 | graph.add_review(reviewers[0], products[0], 0.2) 203 | graph.add_review(reviewers[0], products[1], 0.9) 204 | graph.add_review(reviewers[0], products[2], 0.6) 205 | graph.add_review(reviewers[1], products[0], 0.1) 206 | graph.add_review(reviewers[1], products[1], 0.7) 207 | 208 | # Run the algorithm. 209 | graph.update() 210 | 211 | # Print anomalous reviewers. 212 | print("Anomalous reviewers.") 213 | for r in graph.reviewers: 214 | if r.anomalous_score == 1: 215 | print(r.name) 216 | 217 | # Print summarized rating scores. 218 | print("Summaries.") 219 | for p in graph.products: 220 | print(p.name, p.summary) 221 | 222 | 223 | if __name__ == "__main__": 224 | analyze() 225 | 226 | Note that, the above script uses `click `_. 227 | If you didn't install it, you need to run ``pip install click``. 228 | 229 | Then, you can analyze the graph with a specific :math:`n`, for example 5, 230 | run the script by 231 | 232 | .. code-block:: bash 233 | 234 | ./analyze.py 5 235 | 236 | 237 | Parameter tuning 238 | ^^^^^^^^^^^^^^^^^^ 239 | Basically, bigger :math:`n` produces a better result, too big :math:`n` causes 240 | a worse result. 241 | You need to find the best parameter :math:`n` to obtain the best result. 242 | The best parameter is highly depended on the data you want to analyze. 243 | You should run the algorithm many times. 244 | :ref:`project:parallel_evaluation` may help to evaluation time. 245 | 246 | 247 | API Reference 248 | --------------- 249 | .. toctree:: 250 | :glob: 251 | :maxdepth: 2 252 | 253 | modules/* 254 | 255 | 256 | License 257 | --------- 258 | This software is released under The GNU General Public License Version 3, 259 | see `COPYING `_ for more detail. 260 | 261 | The original FRAUDAR source code, which is in ``fraudar/export``, are made by 262 | `Bryan Hooi `_, 263 | `Hyun Ah Song `_, 264 | `Alex Beutel `_, 265 | `Neil Shah `_, 266 | `Kijung Shin `_, and 267 | `Christos Faloutsos `_, 268 | and licensed under `Apache License, Version 2.0 `_. 269 | 270 | 271 | .. _Review Graph Mining project: https://rgmining.github.io/ 272 | -------------------------------------------------------------------------------- /fraudar/export/greedy.py: -------------------------------------------------------------------------------- 1 | # 2 | # greedy.py 3 | # 4 | # Copyright (c) 2016-2025 Junpei Kawamoto 5 | # 6 | # This file is part of rgmining-fraudar. 7 | # 8 | # rgmining-fraudar is free software: you can redistribute it and/or modify 9 | # it under the terms of the GNU General Public License as published by 10 | # the Free Software Foundation, either version 3 of the License, or 11 | # (at your option) any later version. 12 | # 13 | # rgmining-fraudar is distributed in the hope that it will be useful, 14 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | # GNU General Public License for more details. 17 | # 18 | # You should have received a copy of the GNU General Public License 19 | # along with rgmining-fraudar. If not, see . 20 | # 21 | # This file was originally made by Bryan Hooi et al, 22 | # and distributed under Apache License, Version 2.0. 23 | # 24 | # mypy: ignore-errors 25 | """ 26 | This module contains functions that run the greedy detector for dense regions in a sparse matrix. 27 | use aveDegree or sqrtWeightedAveDegree or logWeightedAveDegree on a sparse matrix, 28 | which returns ((rowSet, colSet), score) for the most suspicious block. 29 | """ 30 | 31 | import random 32 | 33 | import numpy as np 34 | from scipy import sparse 35 | 36 | from .MinTree import MinTree 37 | 38 | np.set_printoptions(linewidth=160) 39 | 40 | 41 | # given 2 lists corresponding to the edge source and destination, 42 | # this returns the sparse matrix representation of the data 43 | # @profile 44 | def listToSparseMatrix(edgesSource, edgesDest): 45 | m = max(edgesSource) + 1 46 | n = max(edgesDest) + 1 47 | M = sparse.coo_matrix( 48 | ([1] * len(edgesSource), (edgesSource, edgesDest)), shape=(m, n) 49 | ) 50 | M1 = M > 0 51 | return M1.astype("int") 52 | 53 | 54 | # reads matrix from file and returns sparse matrix. first 2 columns should be row and column indices of ones. 55 | # @profile 56 | def readData(filename): 57 | # dat = np.genfromtxt(filename, delimiter='\t', dtype=int) 58 | edgesSource = [] 59 | edgesDest = [] 60 | with open(filename) as f: 61 | for line in f: 62 | toks = line.split() 63 | edgesSource.append(int(toks[0])) 64 | edgesDest.append(int(toks[1])) 65 | return listToSparseMatrix(edgesSource, edgesDest) 66 | 67 | 68 | def detectMultiple(M, detectFunc, numToDetect): 69 | Mcur = M.copy().tolil() 70 | res = [] 71 | for i in range(numToDetect): 72 | ((rowSet, colSet), score) = detectFunc(Mcur) 73 | res.append(((rowSet, colSet), score)) 74 | (rs, cs) = Mcur.nonzero() 75 | for i in range(len(rs)): 76 | if rs[i] in rowSet and cs[i] in colSet: 77 | Mcur[rs[i], cs[i]] = 0 78 | return res 79 | 80 | 81 | # inject a clique of size m0 by n0, with density pp. the last parameter testIdx determines the camouflage type. 82 | # testIdx = 1: random camouflage, with camouflage density set so each fraudster outputs approximately equal number 83 | # of fraudulent and camouflage edges 84 | # testIdx = 2: random camouflage, with double the density as in the previous setting 85 | # testIdx = 3: biased camouflage, more likely to add camouflage to high degree columns 86 | def injectCliqueCamo(M, m0, n0, p, testIdx): 87 | (m, n) = M.shape 88 | M2 = M.copy().tolil() 89 | 90 | colSum = np.squeeze(M2.sum(axis=0).A) 91 | colSumPart = colSum[n0:n] 92 | colSumPartPro = np.int_(colSumPart) 93 | colIdx = np.arange(n0, n, 1) 94 | population = np.repeat(colIdx, colSumPartPro, axis=0) 95 | 96 | for i in range(m0): 97 | # inject clique 98 | for j in range(n0): 99 | if random.random() < p: 100 | M2[i, j] = 1 101 | # inject camo 102 | if testIdx == 1: 103 | thres = p * n0 / (n - n0) 104 | for j in range(n0, n): 105 | if random.random() < thres: 106 | M2[i, j] = 1 107 | if testIdx == 2: 108 | thres = 2 * p * n0 / (n - n0) 109 | for j in range(n0, n): 110 | if random.random() < thres: 111 | M2[i, j] = 1 112 | # biased camo 113 | if testIdx == 3: 114 | colRplmt = random.sample(population, int(n0 * p)) 115 | M2[i, colRplmt] = 1 116 | 117 | return M2.tocsc() 118 | 119 | 120 | # sum of weighted edges in rowSet and colSet, plus node suspiciousness values, in matrix M 121 | def c2Score(M, rowSet, colSet, nodeSusp): 122 | suspTotal = ( 123 | nodeSusp[0][list(rowSet)].sum() + nodeSusp[1][list(colSet)].sum() 124 | ) 125 | return M[list(rowSet), :][:, list(colSet)].sum(axis=None) + suspTotal 126 | 127 | 128 | def jaccard(pred, actual): 129 | intersectSize = len(set.intersection(pred[0], actual[0])) + len( 130 | set.intersection(pred[1], actual[1]) 131 | ) 132 | unionSize = len(set.union(pred[0], actual[0])) + len( 133 | set.union(pred[1], actual[1]) 134 | ) 135 | return intersectSize / unionSize 136 | 137 | 138 | def getPrecision(pred, actual): 139 | intersectSize = len(set.intersection(pred[0], actual[0])) + len( 140 | set.intersection(pred[1], actual[1]) 141 | ) 142 | return intersectSize / (len(pred[0]) + len(pred[1])) 143 | 144 | 145 | def getRecall(pred, actual): 146 | intersectSize = len(set.intersection(pred[0], actual[0])) + len( 147 | set.intersection(pred[1], actual[1]) 148 | ) 149 | return intersectSize / (len(actual[0]) + len(actual[1])) 150 | 151 | 152 | def getFMeasure(pred, actual): 153 | prec = getPrecision(pred, actual) 154 | rec = getRecall(pred, actual) 155 | return 0 if (prec + rec == 0) else (2 * prec * rec / (prec + rec)) 156 | 157 | 158 | def getRowPrecision(pred, actual, idx): 159 | intersectSize = len(set.intersection(pred[idx], actual[idx])) 160 | return intersectSize / len(pred[idx]) 161 | 162 | 163 | def getRowRecall(pred, actual, idx): 164 | intersectSize = len(set.intersection(pred[idx], actual[idx])) 165 | return intersectSize / len(actual[idx]) 166 | 167 | 168 | def getRowFMeasure(pred, actual, idx): 169 | prec = getRowPrecision(pred, actual, idx) 170 | rec = getRowRecall(pred, actual, idx) 171 | return 0 if (prec + rec == 0) else (2 * prec * rec / (prec + rec)) 172 | 173 | 174 | # run greedy algorithm using square root column weights 175 | def sqrtWeightedAveDegree(M, nodeSusp=None): 176 | (m, n) = M.shape 177 | colSums = M.sum(axis=0) 178 | colWeights = 1.0 / np.sqrt(np.squeeze(colSums) + 5) 179 | colDiag = sparse.lil_matrix((n, n)) 180 | colDiag.setdiag(colWeights) 181 | W = M * colDiag 182 | return fastGreedyDecreasing(W, colWeights, nodeSusp) 183 | 184 | 185 | # run greedy algorithm using logarithmic weights 186 | def logWeightedAveDegree(M, nodeSusp=None): 187 | (m, n) = M.shape 188 | colSums = M.sum(axis=0) 189 | colWeights = np.squeeze(np.array(1.0 / np.log(np.squeeze(colSums) + 5))) 190 | colDiag = sparse.lil_matrix((n, n)) 191 | colDiag.setdiag(colWeights) 192 | W = M * colDiag 193 | print("finished computing weight matrix") 194 | return fastGreedyDecreasing(W, colWeights, nodeSusp) 195 | 196 | 197 | def aveDegree(M, nodeSusp=None): 198 | (m, n) = M.shape 199 | return fastGreedyDecreasing(M, [1] * n, nodeSusp) 200 | 201 | 202 | def subsetAboveDegree(M, col_thres, row_thres): 203 | M = M.tocsc() 204 | (m, n) = M.shape 205 | colSums = np.squeeze(np.array(M.sum(axis=0))) 206 | rowSums = np.squeeze(np.array(M.sum(axis=1))) 207 | colValid = colSums > col_thres 208 | rowValid = rowSums > row_thres 209 | M1 = M[:, colValid].tocsr() 210 | M2 = M1[rowValid, :] 211 | rowFilter = [i for i in range(m) if rowValid[i]] 212 | colFilter = [i for i in range(n) if colValid[i]] 213 | return M2, rowFilter, colFilter 214 | 215 | 216 | # @profile 217 | def fastGreedyDecreasing(M, colWeights, nodeSusp=None): 218 | (m, n) = M.shape 219 | if nodeSusp is None: 220 | nodeSusp = (np.zeros(m), np.zeros(n)) 221 | Ml = M.tolil() 222 | Mlt = M.transpose().tolil() 223 | rowSet = set(range(0, m)) 224 | colSet = set(range(0, n)) 225 | curScore = c2Score(M, rowSet, colSet, nodeSusp) 226 | 227 | bestAveScore = curScore / (len(rowSet) + len(colSet)) 228 | print("finished initialization") 229 | rowDeltas = ( 230 | np.squeeze(M.sum(axis=1).A) + nodeSusp[0] 231 | ) # contribution of this row to total weight, i.e. *decrease* in total weight when *removing* this row 232 | colDeltas = np.squeeze(M.sum(axis=0).A) + nodeSusp[1] 233 | print("finished setting deltas") 234 | rowTree = MinTree(rowDeltas) 235 | colTree = MinTree(colDeltas) 236 | print("finished building min trees") 237 | 238 | numDeleted = 0 239 | deleted = [] 240 | bestNumDeleted = 0 241 | 242 | while rowSet and colSet: 243 | if (len(colSet) + len(rowSet)) % 100000 == 0: 244 | print("current set size = %d" % (len(colSet) + len(rowSet),)) 245 | (nextRow, rowDelt) = rowTree.getMin() 246 | (nextCol, colDelt) = colTree.getMin() 247 | if rowDelt <= colDelt: 248 | curScore -= rowDelt 249 | for j in Ml.rows[nextRow]: 250 | colTree.changeVal(j, -colWeights[j]) 251 | rowSet -= {nextRow} 252 | rowTree.changeVal(nextRow, float("inf")) 253 | deleted.append((0, nextRow)) 254 | else: 255 | curScore -= colDelt 256 | for i in Mlt.rows[nextCol]: 257 | rowTree.changeVal(i, -colWeights[nextCol]) 258 | colSet -= {nextCol} 259 | colTree.changeVal(nextCol, float("inf")) 260 | deleted.append((1, nextCol)) 261 | 262 | numDeleted += 1 263 | curAveScore = curScore / (len(colSet) + len(rowSet)) 264 | 265 | if curAveScore > bestAveScore: 266 | bestAveScore = curAveScore 267 | bestNumDeleted = numDeleted 268 | 269 | # 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------