├── .gitignore ├── LICENSE.txt ├── MANIFEST.in ├── README.rst ├── docs ├── Makefile ├── authors.rst ├── conf.py ├── contributing.rst ├── index.rst ├── installation.rst ├── make.bat ├── netlsd.kernels.compare.rst ├── netlsd.kernels.heat.rst ├── netlsd.kernels.netlsd.rst ├── netlsd.kernels.wave.rst ├── readme.rst ├── reference.rst └── usage.rst ├── netlsd ├── __init__.py ├── kernels.py └── util.py ├── requirements.txt ├── setup.cfg └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled python modules. 2 | *.pyc 3 | 4 | # Setuptools distribution folder. 5 | /dist/ 6 | 7 | # Python egg metadata, regenerated from source files by setuptools. 8 | /*.egg-info 9 | /*.egg 10 | 11 | # Sphinx 12 | docs/_* -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Anton Tsitsulin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.rst -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | =============================== 2 | NetLSD 3 | =============================== 4 | 5 | NetLSD is a family of spectral graph descriptros. Given a graph, NetLSD computes a low-dimensional vector representation that can be used for different tasks. 6 | 7 | Quick start 8 | ----------- 9 | 10 | .. code-block:: python 11 | 12 | import netlsd 13 | import networkx as nx 14 | 15 | g = nx.erdos_renyi_graph(100, 0.01) # create a random graph with 100 nodes 16 | descriptor = netlsd.heat(g) # compute the signature 17 | 18 | That's it! Then, signatures of two graphs can be compared easily. NetLSD supports `networkx `_, `graph_tool `_, and `igraph `_ packages natively. 19 | 20 | .. code-block:: python 21 | 22 | import netlsd 23 | import numpy as np 24 | 25 | distance = netlsd.compare(desc1, desc2) # compare the signatures using l2 distance 26 | distance = np.linalg.norm(desc1 - desc2) # equivalent 27 | 28 | 29 | For more advanced usage, check out `online documentation `_. 30 | 31 | 32 | Requirements 33 | ------------ 34 | * numpy 35 | * scipy 36 | 37 | 38 | Installation 39 | ------------ 40 | #. cd netlsd 41 | #. pip install -r requirements.txt 42 | #. python setup.py install 43 | 44 | Or simply ``pip install netlsd`` 45 | 46 | Citing 47 | ------ 48 | If you find NetLSD useful in your research, we ask that you cite the following paper:: 49 | 50 | @inproceedings{Tsitsulin:2018:KDD, 51 | author={Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and M{\"u}ller, Emmanuel}, 52 | title={NetLSD: Hearing the Shape of a Graph}, 53 | booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, 54 | series = {KDD '18}, 55 | year = {2018}, 56 | } 57 | 58 | Misc 59 | ---- 60 | 61 | NetLSD - Hearing the shape of graphs. 62 | 63 | * MIT license 64 | * Documentation: http://netlsd.readthedocs.org -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | PAPER = 8 | BUILDDIR = _build 9 | 10 | # User-friendly check for sphinx-build 11 | ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) 12 | $(error The '$(SPHINXBUILD)' command was not found. 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The pseudo-XML files are in $(BUILDDIR)/pseudoxml." 193 | -------------------------------------------------------------------------------- /docs/authors.rst: -------------------------------------------------------------------------------- 1 | ======= 2 | Credits 3 | ======= 4 | 5 | * Anton Tsitsulin -------------------------------------------------------------------------------- /docs/conf.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | # 4 | # NetLSD documentation build configuration file, created by 5 | # sphinx-quickstart on Mon Jun 4 10:50:06 2018. 6 | # 7 | # This file is execfile()d with the current directory set to its 8 | # containing dir. 9 | # 10 | # Note that not all possible configuration values are present in this 11 | # autogenerated file. 12 | # 13 | # All configuration values have a default; values that are commented out 14 | # serve to show the default. 15 | 16 | import sys 17 | import os 18 | import shlex 19 | from recommonmark.parser import CommonMarkParser 20 | 21 | # If extensions (or modules to document with autodoc) are in another directory, 22 | # add these directories to sys.path here. If the directory is relative to the 23 | # documentation root, use os.path.abspath to make it absolute, like shown here. 24 | #sys.path.insert(0, os.path.abspath('.')) 25 | 26 | # -- General configuration ------------------------------------------------ 27 | 28 | # If your documentation needs a minimal Sphinx version, state it here. 29 | #needs_sphinx = '1.0' 30 | source_parsers = { 31 | '.md': CommonMarkParser, 32 | } 33 | 34 | 35 | # Add any Sphinx extension module names here, as strings. They can be 36 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 37 | # ones. 38 | extensions = [ 39 | 'sphinx.ext.autosummary', 40 | 'sphinx.ext.autodoc', 41 | 'sphinx.ext.todo', 42 | 'sphinx.ext.coverage', 43 | 'sphinx.ext.mathjax', 44 | 'sphinx.ext.viewcode', 45 | ] 46 | 47 | # Add any paths that contain templates here, relative to this directory. 48 | templates_path = ['_templates'] 49 | 50 | # The suffix(es) of source filenames. 51 | # You can specify multiple suffix as a list of string: 52 | # source_suffix = ['.rst', '.md'] 53 | source_suffix = ['.rst', '.md'] 54 | 55 | # The encoding of source files. 56 | #source_encoding = 'utf-8-sig' 57 | 58 | # The master toctree document. 59 | master_doc = 'index' 60 | 61 | # General information about the project. 62 | project = 'NetLSD' 63 | copyright = '2018, Anton Tsitsulin' 64 | author = 'Anton Tsitsulin' 65 | 66 | # The version info for the project you're documenting, acts as replacement for 67 | # |version| and |release|, also used in various other places throughout the 68 | # built documents. 69 | # 70 | # The short X.Y version. 71 | version = '0.1' 72 | # The full version, including alpha/beta/rc tags. 73 | release = '0.1' 74 | 75 | # The language for content autogenerated by Sphinx. Refer to documentation 76 | # for a list of supported languages. 77 | # 78 | # This is also used if you do content translation via gettext catalogs. 79 | # Usually you set "language" from the command line for these cases. 80 | language = None 81 | 82 | # There are two options for replacing |today|: either, you set today to some 83 | # non-false value, then it is used: 84 | #today = '' 85 | # Else, today_fmt is used as the format for a strftime call. 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 | exclude_patterns = ['_build'] 91 | 92 | # The reST default role (used for this markup: `text`) to use for all 93 | # documents. 94 | #default_role = None 95 | 96 | # If true, '()' will be appended to :func: etc. cross-reference text. 97 | #add_function_parentheses = True 98 | 99 | # If true, the current module name will be prepended to all description 100 | # unit titles (such as .. function::). 101 | #add_module_names = True 102 | 103 | # If true, sectionauthor and moduleauthor directives will be shown in the 104 | # output. They are ignored by default. 105 | #show_authors = False 106 | 107 | # The name of the Pygments (syntax highlighting) style to use. 108 | pygments_style = 'sphinx' 109 | 110 | # A list of ignored prefixes for module index sorting. 111 | #modindex_common_prefix = [] 112 | 113 | # If true, keep warnings as "system message" paragraphs in the built documents. 114 | #keep_warnings = False 115 | 116 | # If true, `todo` and `todoList` produce output, else they produce nothing. 117 | todo_include_todos = True 118 | 119 | 120 | # -- Options for HTML output ---------------------------------------------- 121 | 122 | # The theme to use for HTML and HTML Help pages. See the documentation for 123 | # a list of builtin themes. 124 | html_theme = 'sphinx_rtd_theme' 125 | 126 | # Theme options are theme-specific and customize the look and feel of a theme 127 | # further. For a list of options available for each theme, see the 128 | # documentation. 129 | #html_theme_options = {} 130 | 131 | # Add any paths that contain custom themes here, relative to this directory. 132 | #html_theme_path = [] 133 | 134 | # The name for this set of Sphinx documents. If None, it defaults to 135 | # " v documentation". 136 | #html_title = None 137 | 138 | # A shorter title for the navigation bar. Default is the same as html_title. 139 | #html_short_title = None 140 | 141 | # The name of an image file (relative to this directory) to place at the top 142 | # of the sidebar. 143 | #html_logo = None 144 | 145 | # The name of an image file (within the static path) to use as favicon of the 146 | # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 147 | # pixels large. 148 | #html_favicon = None 149 | 150 | # Add any paths that contain custom static files (such as style sheets) here, 151 | # relative to this directory. They are copied after the builtin static files, 152 | # so a file named "default.css" will overwrite the builtin "default.css". 153 | html_static_path = ['_static'] 154 | 155 | # Add any extra paths that contain custom files (such as robots.txt or 156 | # .htaccess) here, relative to this directory. These files are copied 157 | # directly to the root of the documentation. 158 | #html_extra_path = [] 159 | 160 | # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, 161 | # using the given strftime format. 162 | #html_last_updated_fmt = '%b %d, %Y' 163 | 164 | # If true, SmartyPants will be used to convert quotes and dashes to 165 | # typographically correct entities. 166 | #html_use_smartypants = True 167 | 168 | # Custom sidebar templates, maps document names to template names. 169 | #html_sidebars = {} 170 | 171 | # Additional templates that should be rendered to pages, maps page names to 172 | # template names. 173 | #html_additional_pages = {} 174 | 175 | # If false, no module index is generated. 176 | #html_domain_indices = True 177 | 178 | # If false, no index is generated. 179 | #html_use_index = True 180 | 181 | # If true, the index is split into individual pages for each letter. 182 | #html_split_index = False 183 | 184 | # If true, links to the reST sources are added to the pages. 185 | #html_show_sourcelink = True 186 | 187 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 188 | #html_show_sphinx = True 189 | 190 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 191 | #html_show_copyright = True 192 | 193 | # If true, an OpenSearch description file will be output, and all pages will 194 | # contain a tag referring to it. The value of this option must be the 195 | # base URL from which the finished HTML is served. 196 | #html_use_opensearch = '' 197 | 198 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 199 | #html_file_suffix = None 200 | 201 | # Language to be used for generating the HTML full-text search index. 202 | # Sphinx supports the following languages: 203 | # 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' 204 | # 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr' 205 | #html_search_language = 'en' 206 | 207 | # A dictionary with options for the search language support, empty by default. 208 | # Now only 'ja' uses this config value 209 | #html_search_options = {'type': 'default'} 210 | 211 | # The name of a javascript file (relative to the configuration directory) that 212 | # implements a search results scorer. If empty, the default will be used. 213 | #html_search_scorer = 'scorer.js' 214 | 215 | # Output file base name for HTML help builder. 216 | htmlhelp_basename = 'NetLSDdoc' 217 | 218 | # -- Options for LaTeX output --------------------------------------------- 219 | 220 | latex_elements = { 221 | # The paper size ('letterpaper' or 'a4paper'). 222 | #'papersize': 'letterpaper', 223 | 224 | # The font size ('10pt', '11pt' or '12pt'). 225 | #'pointsize': '10pt', 226 | 227 | # Additional stuff for the LaTeX preamble. 228 | #'preamble': '', 229 | 230 | # Latex figure (float) alignment 231 | #'figure_align': 'htbp', 232 | } 233 | 234 | # Grouping the document tree into LaTeX files. List of tuples 235 | # (source start file, target name, title, 236 | # author, documentclass [howto, manual, or own class]). 237 | latex_documents = [ 238 | (master_doc, 'NetLSD.tex', 'NetLSD Documentation', 239 | 'Anton Tsitsulin', 'manual'), 240 | ] 241 | 242 | # The name of an image file (relative to this directory) to place at the top of 243 | # the title page. 244 | #latex_logo = None 245 | 246 | # For "manual" documents, if this is true, then toplevel headings are parts, 247 | # not chapters. 248 | #latex_use_parts = False 249 | 250 | # If true, show page references after internal links. 251 | #latex_show_pagerefs = False 252 | 253 | # If true, show URL addresses after external links. 254 | #latex_show_urls = False 255 | 256 | # Documents to append as an appendix to all manuals. 257 | #latex_appendices = [] 258 | 259 | # If false, no module index is generated. 260 | #latex_domain_indices = True 261 | 262 | 263 | # -- Options for manual page output --------------------------------------- 264 | 265 | # One entry per manual page. List of tuples 266 | # (source start file, name, description, authors, manual section). 267 | man_pages = [ 268 | (master_doc, 'netlsd', 'NetLSD Documentation', 269 | [author], 1) 270 | ] 271 | 272 | # If true, show URL addresses after external links. 273 | #man_show_urls = False 274 | 275 | 276 | # -- Options for Texinfo output ------------------------------------------- 277 | 278 | # Grouping the document tree into Texinfo files. List of tuples 279 | # (source start file, target name, title, author, 280 | # dir menu entry, description, category) 281 | texinfo_documents = [ 282 | (master_doc, 'NetLSD', 'NetLSD Documentation', 283 | author, 'NetLSD', 'One line description of project.', 284 | 'Miscellaneous'), 285 | ] 286 | 287 | # Documents to append as an appendix to all manuals. 288 | #texinfo_appendices = [] 289 | 290 | # If false, no module index is generated. 291 | #texinfo_domain_indices = True 292 | 293 | # How to display URL addresses: 'footnote', 'no', or 'inline'. 294 | #texinfo_show_urls = 'footnote' 295 | 296 | # If true, do not generate a @detailmenu in the "Top" node's menu. 297 | #texinfo_no_detailmenu = False 298 | -------------------------------------------------------------------------------- /docs/contributing.rst: -------------------------------------------------------------------------------- 1 | ============ 2 | Contributing 3 | ============ 4 | 5 | Contributions are welcome, and they are greatly appreciated! Every 6 | little bit helps, and credit will always be given. 7 | 8 | You can contribute in many ways: 9 | 10 | Types of Contributions 11 | ---------------------- 12 | 13 | Report Bugs 14 | ~~~~~~~~~~~ 15 | 16 | Report bugs at https://github.com/xgfs/NetLSD/issues. 17 | 18 | If you are reporting a bug, please include: 19 | 20 | * Your operating system name and version. 21 | * Any details about your local setup that might be helpful in troubleshooting. 22 | * Detailed steps to reproduce the bug. 23 | 24 | Fix Bugs 25 | ~~~~~~~~ 26 | 27 | Look through the GitHub issues for bugs. Anything tagged with "bug" 28 | is open to whoever wants to implement it. 29 | 30 | Implement Features 31 | ~~~~~~~~~~~~~~~~~~ 32 | 33 | Look through the GitHub issues for features. Anything tagged with "feature" 34 | is open to whoever wants to implement it. 35 | 36 | Write Documentation 37 | ~~~~~~~~~~~~~~~~~~~ 38 | 39 | NetLSD could always use more documentation, whether as part of the 40 | official NetLSD docs, in docstrings, or even on the web in blog posts, 41 | articles, and such. 42 | 43 | Submit Feedback 44 | ~~~~~~~~~~~~~~~ 45 | 46 | The best way to send feedback is to file an issue at https://github.com/xgfs/NetLSD/issues. 47 | 48 | If you are proposing a feature: 49 | 50 | * Explain in detail how it would work. 51 | * Keep the scope as narrow as possible, to make it easier to implement. 52 | * Remember that this is a volunteer-driven project, and that contributions 53 | are welcome :) 54 | 55 | Get Started! 56 | ------------ 57 | 58 | Ready to contribute? Here's how to set up `NetLSD` for local development. 59 | 60 | 1. Fork the `NetLSD` repo on GitHub. 61 | 2. Clone your fork locally:: 62 | 63 | $ git clone git@github.com:your_name_here/NetLSD.git 64 | 65 | 3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:: 66 | 67 | $ mkvirtualenv NetLSD 68 | $ cd NetLSD/ 69 | $ python setup.py develop 70 | 71 | 4. Create a branch for local development:: 72 | 73 | $ git checkout -b name-of-your-bugfix-or-feature 74 | 75 | Now you can make your changes locally. 76 | 77 | 5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:: 78 | 79 | $ flake8 NetLSD tests 80 | $ python setup.py test 81 | $ tox 82 | 83 | To get flake8 and tox, just pip install them into your virtualenv. 84 | 85 | 6. Commit your changes and push your branch to GitHub:: 86 | 87 | $ git add . 88 | $ git commit -m "Your detailed description of your changes." 89 | $ git push origin name-of-your-bugfix-or-feature 90 | 91 | 7. Submit a pull request through the GitHub website. -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | .. NetLSD documentation master file, created by 2 | sphinx-quickstart on Mon Jun 4 10:50:06 2018. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to NetLSD's documentation! 7 | ================================== 8 | 9 | Contents: 10 | 11 | .. toctree:: 12 | :maxdepth: 2 13 | 14 | readme 15 | installation 16 | usage 17 | reference 18 | contributing 19 | authors 20 | 21 | 22 | Indices and tables 23 | ================== 24 | 25 | * :ref:`genindex` 26 | * :ref:`modindex` 27 | * :ref:`search` 28 | 29 | -------------------------------------------------------------------------------- /docs/installation.rst: -------------------------------------------------------------------------------- 1 | ============ 2 | Installation 3 | ============ 4 | 5 | At the command line:: 6 | 7 | $ pip install netlsd 8 | 9 | Or, if you prefer to install from source:: 10 | 11 | $ mkvirtualenv netlsd 12 | $ pip install netlsd -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | REM Command file for Sphinx documentation 4 | 5 | if "%SPHINXBUILD%" == "" ( 6 | set SPHINXBUILD=sphinx-build 7 | ) 8 | set BUILDDIR=_build 9 | set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . 10 | set I18NSPHINXOPTS=%SPHINXOPTS% . 11 | if NOT "%PAPER%" == "" ( 12 | set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% 13 | set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% 14 | ) 15 | 16 | if "%1" == "" goto help 17 | 18 | if "%1" == "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. devhelp to make HTML files and a Devhelp project 29 | echo. epub to make an epub 30 | echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter 31 | echo. text to make text files 32 | echo. man to make manual pages 33 | echo. texinfo to make Texinfo files 34 | echo. gettext to make PO message catalogs 35 | echo. changes to make an overview over all changed/added/deprecated items 36 | echo. xml to make Docutils-native XML files 37 | echo. pseudoxml to make pseudoxml-XML files for display purposes 38 | echo. linkcheck to check all external links for integrity 39 | echo. doctest to run all doctests embedded in the documentation if enabled 40 | echo. coverage to run coverage check of the documentation if enabled 41 | goto end 42 | ) 43 | 44 | if "%1" == "clean" ( 45 | for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i 46 | del /q /s %BUILDDIR%\* 47 | goto end 48 | ) 49 | 50 | 51 | REM Check if sphinx-build is available and fallback to Python version if any 52 | %SPHINXBUILD% 2> nul 53 | if errorlevel 9009 goto sphinx_python 54 | goto sphinx_ok 55 | 56 | :sphinx_python 57 | 58 | set SPHINXBUILD=python -m sphinx.__init__ 59 | %SPHINXBUILD% 2> nul 60 | if errorlevel 9009 ( 61 | echo. 62 | echo.The 'sphinx-build' command was not found. 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The XML files are in %BUILDDIR%/xml. 252 | goto end 253 | ) 254 | 255 | if "%1" == "pseudoxml" ( 256 | %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml 257 | if errorlevel 1 exit /b 1 258 | echo. 259 | echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. 260 | goto end 261 | ) 262 | 263 | :end 264 | -------------------------------------------------------------------------------- /docs/netlsd.kernels.compare.rst: -------------------------------------------------------------------------------- 1 | netlsd.kernels.compare 2 | ====================== 3 | 4 | .. currentmodule:: netlsd.kernels 5 | 6 | .. autofunction:: compare -------------------------------------------------------------------------------- /docs/netlsd.kernels.heat.rst: -------------------------------------------------------------------------------- 1 | netlsd.kernels.heat 2 | =================== 3 | 4 | .. currentmodule:: netlsd.kernels 5 | 6 | .. autofunction:: heat -------------------------------------------------------------------------------- /docs/netlsd.kernels.netlsd.rst: -------------------------------------------------------------------------------- 1 | netlsd.kernels.netlsd 2 | ===================== 3 | 4 | .. currentmodule:: netlsd.kernels 5 | 6 | .. autofunction:: netlsd -------------------------------------------------------------------------------- /docs/netlsd.kernels.wave.rst: -------------------------------------------------------------------------------- 1 | netlsd.kernels.wave 2 | =================== 3 | 4 | .. currentmodule:: netlsd.kernels 5 | 6 | .. autofunction:: wave -------------------------------------------------------------------------------- /docs/readme.rst: -------------------------------------------------------------------------------- 1 | .. include:: ../README.rst -------------------------------------------------------------------------------- /docs/reference.rst: -------------------------------------------------------------------------------- 1 | **************** 2 | Reference 3 | **************** 4 | .. currentmodule:: netlsd 5 | 6 | NetLSD 7 | ---------- 8 | .. automodule:: netlsd.kernels 9 | 10 | .. autosummary:: 11 | :toctree: . 12 | 13 | netlsd 14 | heat 15 | wave 16 | compare 17 | 18 | -------------------------------------------------------------------------------- /docs/usage.rst: -------------------------------------------------------------------------------- 1 | ============ 2 | Usage 3 | ============ 4 | 5 | Quick start usage 6 | ---------------------- 7 | 8 | .. code-block:: python 9 | 10 | import netlsd 11 | import networkx as nx 12 | 13 | g = nx.erdos_renyi_graph(100, 0.01) # create a random graph with 100 nodes 14 | descriptor = netlsd.heat(g) # compute NetLSD signature 15 | 16 | That's it! Then, signatures of two graphs can be compared easily:: 17 | 18 | .. code-block:: python 19 | 20 | import netlsd 21 | 22 | distance = netlsd.compare(sig1, sig2) # compare the signatures using l2 distance 23 | 24 | or, equivalently:: 25 | 26 | .. code-block:: python 27 | 28 | import numpy as np 29 | 30 | distance = np.linalg.norm(sig1 - sig2) # compare the signatures using l2 distance in numpy 31 | 32 | Advanced usage 33 | ---------------------- 34 | 35 | Here we outline different ways to get more out of NetLSD. 36 | 37 | Try the wave kernel 38 | ~~~~~~~~ 39 | 40 | In the paper, we introduce two kernels: heat and wave. 41 | You can simply replace ``netlsd.heat`` with ``netlsd.wave`` to switch to wave kernel. 42 | Wave kernel is known to preserve symmetries and structures as it acts as a band-pass filter on the spectrum. 43 | 44 | Supply adjacency matrix directly 45 | ~~~~~~~~ 46 | 47 | You do not need to use python's graph libraries to interface with NetLSD. 48 | One option is to use any type of a sparse matrix from scipy: 49 | 50 | .. code-block:: python 51 | 52 | import netlsd 53 | import scipy.sparse as sps 54 | 55 | A = sps.random(1000, 1000) # create a random adjacency matrix 56 | A = A + A.T # make sure it is undirected 57 | descriptor = netlsd.heat(A) # compute NetLSD signature 58 | 59 | In case you have already constructed a Laplacian, just pass it to the function. 60 | 61 | Scale things up with custom eigensolvers 62 | ~~~~~~~~ 63 | 64 | If you want to use a different eigensolver routine, such as SLEPc, you can directly supply eigenvalues to NetLSD: 65 | 66 | .. code-block:: python 67 | 68 | import netlsd 69 | import fancy_eigensolver 70 | 71 | eigenvalues = fancy_eigensolver(graph) 72 | descriptor = netlsd.heat(eigenvalues) # compute NetLSD signature -------------------------------------------------------------------------------- /netlsd/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | __author__ = 'Anton Tsitsulin' 4 | __email__ = 'anton.tsitsulin@hpi.de' 5 | __version__ = '0.0.1' 6 | 7 | from .kernels import heat, wave, netlsd, compare -------------------------------------------------------------------------------- /netlsd/kernels.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from .util import check_1d, check_2d, eigenvalues_auto, graph_to_laplacian, mat_to_laplacian 3 | 4 | 5 | def compare(descriptor1, descriptor2): 6 | """ 7 | Computes the distance between two NetLSD representations. 8 | 9 | Parameters 10 | ---------- 11 | descriptor1: numpy.ndarray 12 | First signature to compare 13 | descriptor2: numpy.ndarray 14 | Second signature to compare 15 | 16 | Returns 17 | ------- 18 | float 19 | NetLSD distance 20 | 21 | """ 22 | return np.linalg.norm(descriptor1-descriptor2) 23 | 24 | 25 | def netlsd(inp, timescales=np.logspace(-2, 2, 250), kernel='heat', eigenvalues='auto', normalization='empty', normalized_laplacian=True): 26 | """ 27 | Computes NetLSD signature from some given input, timescales, and normalization. 28 | 29 | Accepts matrices, common Python graph libraries' graphs, or vectors of eigenvalues. 30 | For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. 31 | 32 | Parameters 33 | ---------- 34 | inp: obj 35 | 2D numpy/scipy matrix, common Python graph libraries' graph, or vector of eigenvalues 36 | timescales : numpy.ndarray 37 | Vector of discrete timesteps for the kernel computation 38 | kernel : str 39 | Either 'heat' or 'wave'. Type of a kernel to use for computation. 40 | eigenvalues : str 41 | Either string or int or tuple 42 | Number of eigenvalues to compute / use for approximation. 43 | If string, we expect either 'full' or 'auto', otherwise error will be raised. 'auto' lets the program decide based on the faithful usage. 'full' computes all eigenvalues. 44 | If int, compute n_eivals eigenvalues from each side and approximate using linear growth approximation. 45 | If tuple, we expect two ints, first for lower part of approximation, and second for the upper part. 46 | normalization : str or numpy.ndarray 47 | Either 'empty', 'complete' or None. 48 | If None or any ther value, return unnormalized heat kernel trace. 49 | For the details how 'empty' and 'complete' are computed, please refer to the paper. 50 | If np.ndarray, they are treated as exact normalization constants 51 | normalized_laplacian: bool 52 | Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. 53 | 54 | Returns 55 | ------- 56 | numpy.ndarray 57 | NetLSD signature 58 | 59 | """ 60 | if kernel not in {'heat', 'wave'}: 61 | raise AttributeError('Unirecognized kernel type: expected one of [\'heat\', \'wave\'], got {0}'.format(kernel)) 62 | if not isinstance(normalized_laplacian, bool): 63 | raise AttributeError('Unknown Laplacian type: expected bool, got {0}'.format(normalized_laplacian)) 64 | if not isinstance(eigenvalues, (int, tuple, str)): 65 | raise AttributeError('Unirecognized requested eigenvalue number: expected type of [\'str\', \'tuple\', or \'int\'], got {0}'.format(type(eigenvalues))) 66 | if not isinstance(timescales, np.ndarray): 67 | raise AttributeError('Unirecognized timescales data type: expected np.ndarray, got {0}'.format(type(timescales))) 68 | if timescales.ndim != 1: 69 | raise AttributeError('Unirecognized timescales dimensionality: expected a vector, got {0}-d array'.format(timescales.ndim)) 70 | if normalization not in {'complete', 'empty', 'none', True, False, None}: 71 | if not isinstance(normalization, np.ndarray): 72 | raise AttributeError('Unirecognized normalization type: expected one of [\'complete\', \'empty\', None or np.ndarray], got {0}'.format(normalization)) 73 | if normalization.ndim != 1: 74 | raise AttributeError('Unirecognized normalization dimensionality: expected a vector, got {0}-d array'.format(normalization.ndim)) 75 | if timescales.shape[0] != normalization.shape[0]: 76 | raise AttributeError('Unirecognized normalization dimensionality: expected {0}-length vector, got length {1}'.format(timescales.shape[0], normalization.shape[0])) 77 | 78 | eivals = check_1d(inp) 79 | if eivals is None: 80 | mat = check_2d(inp) 81 | if mat is None: 82 | mat = graph_to_laplacian(inp, normalized_laplacian) 83 | if mat is None: 84 | raise ValueError('Unirecognized input type: expected one of [\'np.ndarray\', \'scipy.sparse\', \'networkx.Graph\',\' graph_tool.Graph,\' or \'igraph.Graph\'], got {0}'.format(type(inp))) 85 | else: 86 | mat = mat_to_laplacian(inp, normalized_laplacian) 87 | eivals = eigenvalues_auto(mat, eigenvalues) 88 | if kernel == 'heat': 89 | return _hkt(eivals, timescales, normalization, normalized_laplacian) 90 | else: 91 | return _wkt(eivals, timescales, normalization, normalized_laplacian) 92 | 93 | 94 | def heat(inp, timescales=np.logspace(-2, 2, 250), eigenvalues='auto', normalization='empty', normalized_laplacian=True): 95 | """ 96 | Computes heat kernel trace from some given input, timescales, and normalization. 97 | 98 | Accepts matrices, common Python graph libraries' graphs, or vectors of eigenvalues. 99 | For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. 100 | 101 | Parameters 102 | ---------- 103 | inp: obj 104 | 2D numpy/scipy matrix, common Python graph libraries' graph, or vector of eigenvalues 105 | timescales : numpy.ndarray 106 | Vector of discrete timesteps for the kernel computation 107 | eigenvalues : str 108 | Either string or int or tuple 109 | Number of eigenvalues to compute / use for approximation. 110 | If string, we expect either 'full' or 'auto', otherwise error will be raised. 'auto' lets the program decide based on the faithful usage. 'full' computes all eigenvalues. 111 | If int, compute n_eivals eigenvalues from each side and approximate using linear growth approximation. 112 | If tuple, we expect two ints, first for lower part of approximation, and second for the upper part. 113 | normalization : str or numpy.ndarray 114 | Either 'empty', 'complete' or None. 115 | If None or any ther value, return unnormalized heat kernel trace. 116 | For the details how 'empty' and 'complete' are computed, please refer to the paper. 117 | If np.ndarray, they are treated as exact normalization constants 118 | normalized_laplacian: bool 119 | Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. 120 | 121 | Returns 122 | ------- 123 | numpy.ndarray 124 | Heat kernel trace signature 125 | 126 | """ 127 | return netlsd(inp, timescales, 'heat', eigenvalues, normalization, normalized_laplacian) 128 | 129 | 130 | def wave(inp, timescales=np.linspace(0, 2*np.pi, 250), eigenvalues='auto', normalization='empty', normalized_laplacian=True): 131 | """ 132 | Computes wave kernel trace from some given input, timescales, and normalization. 133 | 134 | Accepts matrices, common Python graph libraries' graphs, or vectors of eigenvalues. 135 | For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. 136 | 137 | Parameters 138 | ---------- 139 | inp: obj 140 | 2D numpy/scipy matrix, common Python graph libraries' graph, or vector of eigenvalues 141 | timescales : numpy.ndarray 142 | Vector of discrete timesteps for the kernel computation 143 | eigenvalues : str 144 | Either string or int or tuple 145 | Number of eigenvalues to compute / use for approximation. 146 | If string, we expect either 'full' or 'auto', otherwise error will be raised. 'auto' lets the program decide based on the faithful usage. 'full' computes all eigenvalues. 147 | If int, compute n_eivals eigenvalues from each side and approximate using linear growth approximation. 148 | If tuple, we expect two ints, first for lower part of approximation, and second for the upper part. 149 | normalization : str or numpy.ndarray 150 | Either 'empty', 'complete' or None. 151 | If None or any ther value, return unnormalized wave kernel trace. 152 | For the details how 'empty' and 'complete' are computed, please refer to the paper. 153 | If np.ndarray, they are treated as exact normalization constants 154 | normalized_laplacian: bool 155 | Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. 156 | 157 | Returns 158 | ------- 159 | numpy.ndarray 160 | Wave kernel trace signature 161 | 162 | """ 163 | return netlsd(inp, timescales, 'wave', eigenvalues, normalization, normalized_laplacian) 164 | 165 | 166 | def _hkt(eivals, timescales, normalization, normalized_laplacian): 167 | """ 168 | Computes heat kernel trace from given eigenvalues, timescales, and normalization. 169 | 170 | For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. 171 | 172 | Parameters 173 | ---------- 174 | eivals : numpy.ndarray 175 | Eigenvalue vector 176 | timescales : numpy.ndarray 177 | Vector of discrete timesteps for the kernel computation 178 | normalization : str or numpy.ndarray 179 | Either 'empty', 'complete' or None. 180 | If None or any ther value, return unnormalized heat kernel trace. 181 | For the details how 'empty' and 'complete' are computed, please refer to the paper. 182 | If np.ndarray, they are treated as exact normalization constants 183 | normalized_laplacian: bool 184 | Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. 185 | 186 | Returns 187 | ------- 188 | numpy.ndarray 189 | Heat kernel trace signature 190 | 191 | """ 192 | nv = eivals.shape[0] 193 | hkt = np.zeros(timescales.shape) 194 | for idx, t in enumerate(timescales): 195 | hkt[idx] = np.sum(np.exp(-t * eivals)) 196 | if isinstance(normalization, np.ndarray): 197 | return hkt / normalization 198 | if normalization == 'empty' or normalization == True: 199 | return hkt / nv 200 | if normalization == 'complete': 201 | if normalized_laplacian: 202 | return hkt / (1 + (nv - 1) * np.exp(-timescales)) 203 | else: 204 | return hkt / (1 + nv * np.exp(-nv * timescales)) 205 | return hkt 206 | 207 | 208 | def _wkt(eivals, timescales, normalization, normalized_laplacian): 209 | """ 210 | Computes wave kernel trace from given eigenvalues, timescales, and normalization. 211 | 212 | For precise definition, please refer to "NetLSD: Hearing the Shape of a Graph" by A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, E. Müller. Published at KDD'18. 213 | 214 | Parameters 215 | ---------- 216 | eivals : numpy.ndarray 217 | Eigenvalue vector 218 | timescales : numpy.ndarray 219 | Vector of discrete timesteps for the kernel computation 220 | normalization : str or numpy.ndarray 221 | Either 'empty', 'complete' or None. 222 | If None or any ther value, return unnormalized wave kernel trace. 223 | For the details how 'empty' and 'complete' are computed, please refer to the paper. 224 | If np.ndarray, they are treated as exact normalization constants 225 | normalized_laplacian: bool 226 | Defines whether the eigenvalues came from the normalized Laplacian. It only affects 'complete' normalization. 227 | 228 | Returns 229 | ------- 230 | numpy.ndarray 231 | Wave kernel trace signature 232 | 233 | """ 234 | nv = eivals.shape[0] 235 | wkt = np.zeros(timescales.shape) 236 | for idx, t in enumerate(timescales): 237 | wkt[idx] = np.sum(np.exp(-1j * t * eivals)) 238 | if isinstance(normalization, np.ndarray): 239 | return wkt / normalization 240 | if normalization == 'empty' or normalization == True: 241 | return wkt / nv 242 | if normalization == 'complete': 243 | if normalized_laplacian: 244 | return wkt / (1 + (nv - 1) * np.cos(timescales)) 245 | else: 246 | return wkt / (1 + (nv - 1) * np.cos(nv * timescales)) 247 | return wkt 248 | -------------------------------------------------------------------------------- /netlsd/util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.linalg as spl 3 | import scipy.sparse as sps 4 | import scipy.sparse.linalg as spsl 5 | 6 | 7 | def check_1d(inp): 8 | """ 9 | Check input to be a vector. Converts lists to np.ndarray. 10 | 11 | Parameters 12 | ---------- 13 | inp : obj 14 | Input vector 15 | 16 | Returns 17 | ------- 18 | numpy.ndarray or None 19 | Input vector or None 20 | 21 | Examples 22 | -------- 23 | >>> check_1d([0, 1, 2, 3]) 24 | [0, 1, 2, 3] 25 | 26 | >>> check_1d('test') 27 | None 28 | 29 | """ 30 | if isinstance(inp, list): 31 | return check_1d(np.array(inp)) 32 | if isinstance(inp, np.ndarray): 33 | if inp.ndim == 1: # input is a vector 34 | return inp 35 | 36 | 37 | def check_2d(inp): 38 | """ 39 | Check input to be a matrix. Converts lists of lists to np.ndarray. 40 | 41 | Also allows the input to be a scipy sparse matrix. 42 | 43 | Parameters 44 | ---------- 45 | inp : obj 46 | Input matrix 47 | 48 | Returns 49 | ------- 50 | numpy.ndarray, scipy.sparse or None 51 | Input matrix or None 52 | 53 | Examples 54 | -------- 55 | >>> check_2d([[0, 1], [2, 3]]) 56 | [[0, 1], [2, 3]] 57 | 58 | >>> check_2d('test') 59 | None 60 | 61 | """ 62 | if isinstance(inp, list): 63 | return check_2d(np.array(inp)) 64 | if isinstance(inp, (np.ndarray, np.matrixlib.defmatrix.matrix)): 65 | if inp.ndim == 2: # input is a dense matrix 66 | return inp 67 | if sps.issparse(inp): 68 | if inp.ndim == 2: # input is a sparse matrix 69 | return inp 70 | 71 | 72 | def graph_to_laplacian(G, normalized=True): 73 | """ 74 | Converts a graph from popular Python packages to Laplacian representation. 75 | 76 | Currently support NetworkX, graph_tool and igraph. 77 | 78 | Parameters 79 | ---------- 80 | G : obj 81 | Input graph 82 | normalized : bool 83 | Whether to use normalized Laplacian. 84 | Normalized and unnormalized Laplacians capture different properties of graphs, e.g. normalized Laplacian spectrum can determine whether a graph is bipartite, but not the number of its edges. We recommend using normalized Laplacian. 85 | 86 | Returns 87 | ------- 88 | scipy.sparse 89 | Laplacian matrix of the input graph 90 | 91 | Examples 92 | -------- 93 | >>> graph_to_laplacian(nx.complete_graph(3), 'unnormalized').todense() 94 | [[ 2, -1, -1], [-1, 2, -1], [-1, -1, 2]] 95 | 96 | >>> graph_to_laplacian('test') 97 | None 98 | 99 | """ 100 | try: 101 | import networkx as nx 102 | if isinstance(G, nx.Graph): 103 | if normalized: 104 | return nx.normalized_laplacian_matrix(G) 105 | else: 106 | return nx.laplacian_matrix(G) 107 | except ImportError: 108 | pass 109 | try: 110 | import graph_tool.all as gt 111 | if isinstance(G, gt.Graph): 112 | if normalized: 113 | return gt.laplacian_type(G, normalized=True) 114 | else: 115 | return gt.laplacian(G) 116 | except ImportError: 117 | pass 118 | try: 119 | import igraph as ig 120 | if isinstance(G, ig.Graph): 121 | if normalized: 122 | return np.array(G.laplacian(normalized=True)) 123 | else: 124 | return np.array(G.laplacian()) 125 | except ImportError: 126 | pass 127 | 128 | 129 | def mat_to_laplacian(mat, normalized): 130 | """ 131 | Converts a sparse or dence adjacency matrix to Laplacian. 132 | 133 | Parameters 134 | ---------- 135 | mat : obj 136 | Input adjacency matrix. If it is a Laplacian matrix already, return it. 137 | normalized : bool 138 | Whether to use normalized Laplacian. 139 | Normalized and unnormalized Laplacians capture different properties of graphs, e.g. normalized Laplacian spectrum can determine whether a graph is bipartite, but not the number of its edges. We recommend using normalized Laplacian. 140 | 141 | Returns 142 | ------- 143 | obj 144 | Laplacian of the input adjacency matrix 145 | 146 | Examples 147 | -------- 148 | >>> mat_to_laplacian(numpy.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]), False) 149 | [[ 2, -1, -1], [-1, 2, -1], [-1, -1, 2]] 150 | 151 | """ 152 | if sps.issparse(mat): 153 | if np.all(mat.diagonal()>=0): # Check diagonal 154 | if np.all((mat-sps.diags(mat.diagonal())).data <= 0): # Check off-diagonal elements 155 | return mat 156 | else: 157 | if np.all(np.diag(mat)>=0): # Check diagonal 158 | if np.all(mat - np.diag(mat) <= 0): # Check off-diagonal elements 159 | return mat 160 | deg = np.squeeze(np.asarray(mat.sum(axis=1))) 161 | if sps.issparse(mat): 162 | L = sps.diags(deg) - mat 163 | else: 164 | L = np.diag(deg) - mat 165 | if not normalized: 166 | return L 167 | with np.errstate(divide='ignore'): 168 | sqrt_deg = 1.0 / np.sqrt(deg) 169 | sqrt_deg[sqrt_deg==np.inf] = 0 170 | if sps.issparse(mat): 171 | sqrt_deg_mat = sps.diags(sqrt_deg) 172 | else: 173 | sqrt_deg_mat = np.diag(sqrt_deg) 174 | return sqrt_deg_mat.dot(L).dot(sqrt_deg_mat) 175 | 176 | 177 | def updown_linear_approx(eigvals_lower, eigvals_upper, nv): 178 | """ 179 | Approximates Laplacian spectrum using upper and lower parts of the eigenspectrum. 180 | 181 | Parameters 182 | ---------- 183 | eigvals_lower : numpy.ndarray 184 | Lower part of the spectrum, sorted 185 | eigvals_upper : numpy.ndarray 186 | Upper part of the spectrum, sorted 187 | nv : int 188 | Total number of nodes (eigenvalues) in the graph. 189 | 190 | Returns 191 | ------- 192 | numpy.ndarray 193 | Vector of approximated eigenvalues 194 | 195 | Examples 196 | -------- 197 | >>> updown_linear_approx([1, 2, 3], [7, 8, 9], 9) 198 | array([1, 2, 3, 4, 5, 6, 7, 8, 9]) 199 | 200 | """ 201 | nal = len(eigvals_lower) 202 | nau = len(eigvals_upper) 203 | if nv < nal + nau: 204 | raise ValueError('Number of supplied eigenvalues ({0} lower and {1} upper) is higher than number of nodes ({2})!'.format(nal, nau, nv)) 205 | ret = np.zeros(nv) 206 | ret[:nal] = eigvals_lower 207 | ret[-nau:] = eigvals_upper 208 | ret[nal-1:-nau+1] = np.linspace(eigvals_lower[-1], eigvals_upper[0], nv-nal-nau+2) 209 | return ret 210 | 211 | 212 | def eigenvalues_auto(mat, n_eivals='auto'): 213 | """ 214 | Automatically computes the spectrum of a given Laplacian matrix. 215 | 216 | Parameters 217 | ---------- 218 | mat : numpy.ndarray or scipy.sparse 219 | Laplacian matrix 220 | n_eivals : string or int or tuple 221 | Number of eigenvalues to compute / use for approximation. 222 | If string, we expect either 'full' or 'auto', otherwise error will be raised. 'auto' lets the program decide based on the faithful usage. 'full' computes all eigenvalues. 223 | If int, compute n_eivals eigenvalues from each side and approximate using linear growth approximation. 224 | If tuple, we expect two ints, first for lower part of approximation, and second for the upper part. 225 | 226 | Returns 227 | ------- 228 | np.ndarray 229 | Vector of approximated eigenvalues 230 | 231 | Examples 232 | -------- 233 | >>> eigenvalues_auto(numpy.array([[ 2, -1, -1], [-1, 2, -1], [-1, -1, 2]]), 'auto') 234 | array([0, 3, 3]) 235 | 236 | """ 237 | do_full = True 238 | n_lower = 150 239 | n_upper = 150 240 | nv = mat.shape[0] 241 | if n_eivals == 'auto': 242 | if mat.shape[0] > 1024: 243 | do_full = False 244 | if n_eivals == 'full': 245 | do_full = True 246 | if isinstance(n_eivals, int): 247 | n_lower = n_upper = n_eivals 248 | do_full = False 249 | if isinstance(n_eivals, tuple): 250 | n_lower, n_upper = n_eivals 251 | do_full = False 252 | if do_full and sps.issparse(mat): 253 | mat = mat.todense() 254 | if sps.issparse(mat): 255 | if n_lower == n_upper: 256 | tr_eivals = spsl.eigsh(mat, 2*n_lower, which='BE', return_eigenvectors=False) 257 | return updown_linear_approx(tr_eivals[:n_upper], tr_eivals[n_upper:], nv) 258 | else: 259 | lo_eivals = spsl.eigsh(mat, n_lower, which='SM', return_eigenvectors=False)[::-1] 260 | up_eivals = spsl.eigsh(mat, n_upper, which='LM', return_eigenvectors=False) 261 | return updown_linear_approx(lo_eivals, up_eivals, nv) 262 | else: 263 | if do_full: 264 | return spl.eigvalsh(mat) 265 | else: 266 | lo_eivals = spl.eigvalsh(mat, eigvals=(0, n_lower-1)) 267 | up_eivals = spl.eigvalsh(mat, eigvals=(nv-n_upper-1, nv-1)) 268 | return updown_linear_approx(lo_eivals, up_eivals, nv) -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scipy -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.rst -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | import sys 5 | 6 | 7 | try: 8 | from setuptools import setup 9 | except ImportError: 10 | from distutils.core import setup 11 | 12 | setup( 13 | name='NetLSD', 14 | version='1.0.2', 15 | author='Anton Tsitsulin', 16 | author_email='anton.tsitsulin@hpi.de', 17 | description='NetLSD descriptors for graphs. Compare and analyze graph structure on multiple levels!', 18 | packages=['netlsd',], 19 | url='http://github.com/xgfs/netlsd', 20 | download_url = 'https://github.com/xgfs/netlsd/archive/0.1.tar.gz', 21 | license='MIT', 22 | install_requires=[ 23 | 'numpy', 24 | 'scipy' 25 | ], 26 | classifiers=[ 27 | 'Development Status :: 3 - Alpha', 28 | 'License :: OSI Approved :: MIT License', 29 | 'Programming Language :: Python :: 2.7', 30 | 'Programming Language :: Python :: 3.5', 31 | 'Topic :: Scientific/Engineering :: Mathematics', 32 | 'Topic :: Scientific/Engineering :: Artificial Intelligence', 33 | ], 34 | long_description=open('README.rst').read(), 35 | ) --------------------------------------------------------------------------------