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.
Description
Forman-Ricci curvature and Persistent homology of unweighted complex networks
%0 Generic
%1 roy2019formanricci
%A Roy, Indrava
%A Vijayaraghavan, Sudharsan
%A Ramaia, Sarath Jyotsna
%A Samal, Areejit
%D 2019
%K TDA network_analysis topology
%T Forman-Ricci curvature and Persistent homology of unweighted complex
networks
%U http://arxiv.org/abs/1912.11337
%X 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.
@preprint{roy2019formanricci,
abstract = {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.},
added-at = {2019-12-26T06:25:24.000+0100},
author = {Roy, Indrava and Vijayaraghavan, Sudharsan and Ramaia, Sarath Jyotsna and Samal, Areejit},
biburl = {https://www.bibsonomy.org/bibtex/28befe08958994856ae73dfdc4f468751/j.c.m.janssen},
description = {Forman-Ricci curvature and Persistent homology of unweighted complex networks},
interhash = {701f0a903946faa468aa498090d7a0a2},
intrahash = {8befe08958994856ae73dfdc4f468751},
keywords = {TDA network_analysis topology},
note = {cite arxiv:1912.11337Comment: 25 pages, 6 Main figures, 10 SI figures},
timestamp = {2019-12-26T06:25:24.000+0100},
title = {Forman-Ricci curvature and Persistent homology of unweighted complex
networks},
url = {http://arxiv.org/abs/1912.11337},
year = 2019
}