Overview¶
GraphRicciCurvature¶
This work computes the Ollivier-Ricci Curvature [1], Ollivier-Ricci Flow [2] [3] and Forman-Ricci Curvature (or Forman curvature) [4].
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 negative curved surface like saddle, these two paths tend to be apart.
In [1], we observe that the edge Ricci curvature play an important role in graph structure. An edge with positive curvature represents an edge within a cluster, while a negatively curved edge tents 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 [3], this “Ricci flow metric” is shown to be able to detect communities.
Both Ricci curvature and Ricci flow metric can be act as a graph fingerprint. Different graph gives different edge Ricci curvature distributions and different Ricci flow metric.
Video demonstration of Ricci flow for community detection:
Package Requirement¶
Installation¶
Installing via pip¶
pip3 install [--user] GraphRicciCurvature
- From version 0.4.0, in order to support larger graph, we switch to NetworKit’s pairwise bidirectional dijkstra algorithm for density distribution (NetworKit>6.0 is required). If the installation of NetworKit failed, please refer to [NetworKit’ Installation instructions]. In most of the cast build this package from source is recommended.
Getting Started¶
- See the jupyter notebook tutorial on [nbviewer] 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] (no account required) or [Google colab] (Faster but Google account required).
- Check the Documentations.
Simple Example¶
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)
More example in example.py.
Contact¶
Please contact [Chien-Chun Ni].
Reference¶
| [1] | (1, 2) 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] |
| [2] | Ni, C.-C., Lin, Y.-Y., Gao, J., and Gu, X. 2018. Network Alignment by Discrete Ollivier-Ricci Flow, Graph Drawing 2018, [arXiv] |
| [3] | (1, 2) Ni, C.-C., Lin, Y.-Y., Luo, F. and Gao, J. 2019. Community Detection on Networks with Ricci Flow, Scientific Reports, [arXiv] |
| [4] | 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] |