Preprint 17/2020

Forman-Ricci curvature and Persistent homology of unweighted complex networks

Indrava Roy, Sudharsan Vijayaraghavan, Sarath Jyotsna Ramaia, and Areejit Samal

Contact the author: Please use for correspondence this email.
Submission date: 29. Jan. 2020
Pages: 27
Download full preprint: PDF (2476 kB)
Link to arXiv: See the arXiv entry of this preprint.

We present the application of topological data analysis (TDA) to study unweighted complex networks via their persistent homology. By endowing appropriate weights that capture the inherent topological characteristics of such a network, we convert an unweighted network into a weighted one. Standard TDA tools are then used to compute their persistent homology. To this end, we use two main quantifiers: a local measure based on Forman's discretized version of Ricci curvature, and a global measure based on edge betweenness centrality. We have employed these methods to study various model and real-world networks. Our results show that persistent homology can be used to distinguish between model and real networks with different topological properties.

06.02.2020, 11:32