Homology Preserving Graph Compression

Abstract

Recently, topological data analysis (TDA) that studies the shape of data by extracting its topological features has become popular in applied network science. Although recent methods show promising performance for various applications, enormous sizes of real-world networks make the existing TDA solutions for graph mining problems hard to adapt with the high computation and space costs. This paper presents a graph compression method to reduce the size of the graph while preserving homology and persistent homology, which are the popular tools in TDA. The experimental studies in real-world large-scale graphs validate the efficiency of the proposed compression method.