2 |
3 | **Note**: This is the repository of an old version of pathpy. It will soon be replaced by [pathpy 3](https://github.com/pathpy/pathpy), which has a new [home on gitHub](https://github.com/pathpy/pathpy).
4 |
5 | # Introduction
6 |
7 | `pathpy` is an OpenSource python package for the modeling and analysis of pathways and temporal networks
8 | using **higher-order** and **multi-order** graphical models.
9 |
10 | The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths
11 | observed in an underlying graph topology. Examples for such data include user click streams in information networks,
12 | biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks,
13 | `pathpy` also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede)
14 | the package [`pyTempnets`](https://github.com/IngoScholtes/pyTempNets).
15 |
16 | `pathpy` facilitates the analysis of temporal correlations in such sequential data. It uses a principled model selection
17 | technique to infer higher-order graphical representations that capture both topological and temporal
18 | characteristics of time-resolved relational data. It specifically allows to answer the question whether a (first-order) network
19 | abstraction of such data is justified, or whether higher-order network abstractions are needed.
20 |
21 | The theoretical foundation of this package, **higher-order network models**, has been developed in the following research works:
22 |
23 | 1. I Scholtes: [When is a network a network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](https://arxiv.org/abs/1702.05499), to appear in KDD'17, arXiv:1702.05499
24 | 2. I Scholtes, N Wider, A Garas: [Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities](http://dx.doi.org/10.1140/epjb/e2016-60663-0), The European Physical Journal B, 89:61, March 2016
25 | 3. I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer: [Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks](http://www.nature.com/ncomms/2014/140924/ncomms6024/full/ncomms6024.html), Nature Communications, 5, September 2014
26 | 4. R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer: [Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.198701), Phys Rev Lett, 110(19), 198701, May 2013
27 |
28 | `pathpy` extends this approach towards **multi-layer graphical models** that capture temporal correlations in pathways at multiple length scales simultaneously. An illustrative example for
29 | a collection of pathways (left) and a multi-order graphical representation of these pathways is shown below. All mathematical details of the framework can be found in this [recent research paper](https://arxiv.org/abs/1702.05499).
30 |
31 | [](https://www.youtube.com/watch?v=CxJkVrD2ZlM)
32 |
33 | 
34 |
35 | # Download and installation
36 |
37 | `pathpy` is pure python code. It has no platform-specific dependencies and should thus work on all platforms. It builds on `numpy` and `scipy`. The latest version of `pathpy` can be installed by typing:
38 |
39 | `> pip install git+git://github.com/IngoScholtes/pathpy.git`
40 |
41 | `pathpy` currently requires python 3.x. We are planning to make the next version backwards compatible with python 2.x.
42 |
43 | # Tutorial
44 |
45 | A [comprehensive educational tutorial](https://ingoscholtes.github.io/pathpy/tutorial.html) which shows how you can use `pathpy` to analyze data on pathways and temporal networks is [available online](https://ingoscholtes.github.io/pathpy/tutorial.html).
46 | Moreover, a tutorial which illustrates the abstraction of **higher-order networks** in the modeling of dynamical processes in temporal networks is [available here](https://www.sg.ethz.ch/team/people/ischoltes/research-insights/temporal-networks-demo/). The
47 | latter tutorial is based on the predecessor library [`pyTempNets`](https://github.com/IngoScholtes/pyTempNets). Most of its features have been ported to `pathpy`.
48 |
49 | # Documentation
50 |
51 | The code is fully documented via docstrings which are accessible through python's built-in help system. Just type `help(SYMBOL_NAME)` to see the documentation of a class or method. A [reference manual is available here](https://ingoscholtes.github.io/pathpy/hierarchy.html).
52 |
53 | # Releases and Versioning
54 |
55 | The first public beta release of pathpy (released February 17 2017) is [v1.0-beta](https://github.com/IngoScholtes/pathpy/releases/tag/v1.0-beta.1). Following versions are named MAJOR.MINOR.PATCH according to [semantic versioning](http://semver.org/). The date of each release is encoded in the PATCH version.
56 |
57 | # Acknowledgements
58 |
59 | The research behind this data analysis framework was funded by the Swiss State Secretariat for Education, Research and Innovation [(Grant C14.0036)](https://www.sg.ethz.ch/projects/seri-information-spaces/). The development of this package was generously supported by the [MTEC Foundation](http://www.mtec.ethz.ch/research/support/MTECFoundation.html) in the context of the project [The Influence of Interaction Patterns on Success in Socio-Technical Systems: From Theory to Practice](https://www.sg.ethz.ch/projects/mtec-interaction-patterns/).
60 |
61 | # Contributors
62 |
63 | [Ingo Scholtes](http://www.ingoscholtes.net) (project lead, development)
64 | [Luca Verginer](http://www.verginer.eu/about/) (development, testing)
65 |
66 | # Past Contributors
67 | Roman Cattaneo (development)
68 | Nicolas Wider (testing)
69 |
70 | # Copyright
71 |
72 | pathpy is licensed under the [GNU Affero General Public License](https://choosealicense.com/licenses/agpl-3.0/).
73 |
74 | (c) Copyright ETH Zürich, Chair of Systems Design, 2015-2018
75 |
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