Stable and Discriminative Topological Graph Analysis

TL;DR

This paper introduces a new topological graph analysis method based on persistent homology, which maps graphs to weighted forms to extract stable and discriminative topological features, outperforming existing methods.

Contribution

The paper presents a novel persistent homology-based approach for topological graph analysis that is stable and discriminative, with demonstrated advantages over existing techniques.

Findings

The proposed method is stable under graph perturbations.

It accurately discriminates between different graph topologies.

Experimental results confirm its effectiveness on real-world and random graphs.

Abstract

We propose a novel method for topological analysis of unweighted graphs which is based on \textit{persistent homology}. The proposed method maps the input graph to a complete weighted graph where the weighting function maps each edge to a value indicating the degree to which it belongs to a clique. The persistent homology of this weighted graph is subsequently computed to give a topological representation describing the topological features of the input graph plus their significance. A formal and experimental analysis of the proposed and existing methods for topological graph analysis is presented. Through this analysis, we find that the proposed method possesses the properties of being stable and performing accurate discrimination. Therefore this method can make accurate inferences regarding the topological features of a given graph. On the other hand, we find that the existing methods considered do not possess these properties making it difficult from them to make such inferences. These findings are experimentally demonstrated using a number of random and real world graphs.