Metadata-Version: 2.1
Name: GraphRicciCurvature
Version: 0.5.0
Summary: Compute discrete Ricci curvatures and Ricci flow on NetworkX graphs.
Home-page: https://github.com/saibalmars/GraphRicciCurvature
Author: Chien-Chun Ni
Author-email: saibalmars@gmail.com
License: UNKNOWN
Description: # GraphRicciCurvature
        A Python library to compute Discrete Ricci curvature, Ricci flow, and Ricci community on NetworkX graph.
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/saibalmars/GraphRicciCurvature/master?filepath=notebooks%2Ftutorial.ipynb)
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        -----
        This work computes the **Ollivier-Ricci Curvature**[Ni], **Ollivier-Ricci Flow**[Ni2,Ni3], **Forman-Ricci Curvature**(or **Forman curvature**)[Sreejith], and **Ricci community**[Ni3] detected by Ollivier-Ricci flow metric.
        
        <p align="center">
        <img src="https://github.com/saibalmars/GraphRicciCurvature/raw/master/doc/_static/rf-manifold.png" title="Manifold Ricci flow" width="300" >
        <img src="https://github.com/saibalmars/GraphRicciCurvature/raw/master/doc/_static/karate_demo.png" title="karate club demo" width="500" >
        </p>
        
        Curvature is a geometric property to describe the local shape of an object. If we draw two parallel paths on a surface with positive curvature like a sphere, these two paths move closer to each other while for a negatively curved surface like a saddle, these two paths tend to be apart.
        
        In [Ni], we observe that the edge Ricci curvature plays an important role in the graph structure. An edge with positive curvature represents an edge within a cluster, while a negatively curved edge tent to be a bridge within clusters. Also, negatively curved edges are highly related to graph connectivity, with negatively curved edges removed from a connected graph, the graph soon become disconnected.
        
        Ricci flow is a process to uniformized the edge Ricci curvature of the graph. For a given graph, the Ricci flow gives a "Ricci flow metric" on each edge as edge weights, such that under these edge weights, the Ricci curvature of the graph is mostly equal everywhere. In [Ni3], this "Ricci flow metric" is shown to be able to detect communities.
        
        Both Ricci curvature and Ricci flow metric can act as a graph fingerprint for graph classification. The different graph gives different edge Ricci curvature distributions and different Ricci flow metric.
        
        Video demonstration of Ricci flow for community detection:
        <p align="center">
        <a href="https://youtu.be/QlENb_XlJ_8?t=20">
        <img src="https://github.com/saibalmars/GraphRicciCurvature/raw/master/doc/_static/ricci_community.png" title="Ricci Community" width="600" >
        </a>
        </p>
        
        ## Package Requirement
        
        * [NetworkX](https://github.com/networkx/networkx) >= 2.0 (Based Graph library)
        * [NetworKit](https://github.com/kit-parco/networkit) >= 6.1 (Shortest path algorithm)
        * [CVXPY](https://github.com/cvxgrp/cvxpy) (LP solver for Optimal transportation)
        * [NumPy](https://github.com/numpy/numpy) (CVXPY support)
        * [POT](https://github.com/rflamary/POT) (For approximate Optimal transportation distance)
        
        
        
        ## Installation
        
        ### Installing via pip
        
        ```bash
        pip3 install [--user] GraphRicciCurvature
        ```
        
        - From version 0.4.0, NetworKit is required to compute shortest path for density distribution. If the installation of NetworKit failed, please refer to [NetworKit' Installation instructions](https://github.com/networkit/networkit#installation-instructions).
        
        ### Upgrading via pip
        
        To run with the latest code for the best performance, upgrade GraphRicciCurvature to the latest version with pip: 
        ```bash
        pip3 install [--user] --upgrade GraphRicciCurvature
        ``` 
        
        ## Getting Started
        - Check the jupyter notebook tutorial on [nbviewer](https://nbviewer.jupyter.org/github/saibalmars/GraphRicciCurvature/blob/master/notebooks/tutorial.ipynb) or [github](notebooks/tutorial.ipynb) for a walk through for the basic usage of Ricci curvature, Ricci flow, and Ricci flow for community detection.
        - Or you can run it in directly on [binder](https://mybinder.org/v2/gh/saibalmars/GraphRicciCurvature/master?filepath=notebooks%2Ftutorial.ipynb) (no account required) or [Google colab](https://colab.research.google.com/github/saibalmars/GraphRicciCurvature/blob/master/notebooks/tutorial.ipynb) (Faster but Google account required).
        - Check the [Documentations](https://graphriccicurvature.readthedocs.io/en/latest/).
        - **Try out [sample graphs](https://github.com/saibalmars/RicciFlow-SampleGraphs) with precomputed Ricci curvature/flow.** 
        
        ## Simple Example
        
        ```python
        import networkx as nx
        from GraphRicciCurvature.OllivierRicci import OllivierRicci
        from GraphRicciCurvature.FormanRicci import FormanRicci
        
        print("\n- Import an example NetworkX karate club graph")
        G = nx.karate_club_graph()
        
        print("\n===== Compute the Ollivier-Ricci curvature of the given graph G =====")
        # compute the Ollivier-Ricci curvature of the given graph G
        orc = OllivierRicci(G, alpha=0.5, verbose="INFO")
        orc.compute_ricci_curvature()
        print("Karate Club Graph: The Ollivier-Ricci curvature of edge (0,1) is %f" % orc.G[0][1]["ricciCurvature"])
        
        print("\n===== Compute the Forman-Ricci curvature of the given graph G =====")
        frc = FormanRicci(G)
        frc.compute_ricci_curvature()
        print("Karate Club Graph: The Forman-Ricci curvature of edge (0,1) is %f" % frc.G[0][1]["formanCurvature"])
        
        # -----------------------------------
        print("\n=====  Compute Ricci flow metric - Optimal Transportation Distance =====")
        G = nx.karate_club_graph()
        orc_OTD = OllivierRicci(G, alpha=0.5, method="OTD", verbose="INFO")
        orc_OTD.compute_ricci_flow(iterations=10)
        print("\n=====  Compute Ricci community - by Ricci flow =====")
        clustering = orc_OTD.ricci_community()
        
        ```
        
        More example in [example.py](example.py).
        
        
        ----
        ## Related Works
        
        - Curvature Graph Network (ICLR2020)  [openreview](https://openreview.net/forum?id=BylEqnVFDB), [code](https://github.com/yeze16159/CurvGN)
        
        
        ## Reference
        
        [Ni]: Ni, C.-C., Lin, Y.-Y., Gao, J., Gu, X., and Saucan, E. 2015. "Ricci curvature of the Internet topology" (Vol. 26, pp. 2758–2766). Presented at the 2015 IEEE Conference on Computer Communications (INFOCOM), IEEE. [arXiv](https://arxiv.org/abs/1501.04138)
        
        [Ni2]: Ni, C.-C., Lin, Y.-Y., Gao, J., and Gu, X. 2018. "Network Alignment by Discrete Ollivier-Ricci Flow", Graph Drawing 2018, [arXiv](https://arxiv.org/abs/1809.00320)
        
        [Ni3]: Ni, C.-C., Lin, Y.-Y., Luo, F. and Gao, J. 2019. "Community Detection on Networks with Ricci Flow", Scientific Reports, [arXiv](https://arxiv.org/abs/1907.03993)
        
        [Sreejith]: Sreejith, R. P., Karthikeyan Mohanraj, Jürgen Jost, Emil Saucan, and Areejit Samal. 2016. “Forman Curvature for Complex Networks.” Journal of Statistical Mechanics: Theory and Experiment 2016 (6). IOP Publishing: 063206. [arxiv](https://arxiv.org/abs/1603.00386)
        
        
        ## Contact
        
        Please contact [Chien-Chun Ni](http://www3.cs.stonybrook.edu/~chni/).
        
        
        ## Cite
        
        If you use this code in your research, please considering cite our paper:
        
        ```
        @article{ni2019community,
          title={Community detection on networks with ricci flow},
          author={Ni, Chien-Chun and Lin, Yu-Yao and Luo, Feng and Gao, Jie},
          journal={Scientific reports},
          volume={9},
          number={1},
          pages={1--12},
          year={2019},
          publisher={Nature Publishing Group}
        }
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
