SASH: Decoding Community Structure in Graphs

TL;DR

This paper introduces SASH, a novel algorithm for decoding community structures in graphs, leveraging an encoding approach and demonstrating effectiveness on simulated and real datasets.

Contribution

The paper presents a new encoding scheme for community detection and a decoding algorithm, SASH, which improves community identification in graphs.

Findings

SASH effectively detects communities in simulated models.

SASH successfully identifies communities in Zachary's Karate Club dataset.

The method outperforms existing approaches in certain scenarios.

Abstract

Detection of communities in a graph entails identifying clusters of densely connected vertices; the area has a variety of important applications and a rich literature. The problem has previously been situated in the realm of error correcting codes by viewing a graph as a noisy version of the assumed underlying communities. In this paper, we introduce an encoding of community structure along with the resulting code's parameters. We then present a novel algorithm, SASH, to decode to estimated communities given an observed dataset. We demonstrate the performance of SASH via simulations on an assortative planted partition model and on the Zachary's Karate Club dataset.