An Empirical Study of Compression-friendly Community Detection Methods

TL;DR

This paper empirically compares community detection methods based on clique-like communities and hub-spoke structures to evaluate their effectiveness in graph compression across ten real-world datasets.

Contribution

It provides an empirical evaluation of two different graph compression techniques, highlighting their relative performance and conditions for effectiveness.

Findings

Both methods can achieve good compression results under favorable conditions.

The study uses two cost functions to compare the approaches.

Experiments conducted on ten real-world graphs.

Abstract

Real-world graphs are massive in size and we need a huge amount of space to store them. Graph compression allows us to compress a graph so that we need a lesser number of bits per link to store it. Of many techniques to compress a graph, a typical approach is to find clique-like caveman or traditional communities in a graph and encode those cliques to compress the graph. On the other side, an alternative approach is to consider graphs as a collection of hubs connecting spokes and exploit it to arrange the nodes such that the resulting adjacency matrix of the graph can be compressed more efficiently. We perform an empirical comparison of these two approaches and show that both methods can yield good results under favorable conditions. We perform our experiments on ten real-world graphs and define two cost functions to present our findings.