MOHCS: Towards Mining Overlapping Highly Connected Subgraphs

TL;DR

This paper introduces MOHCS, an efficient algorithm for mining overlapping highly connected subgraphs in networks, supported by theoretical properties and experimental validation on real and synthetic data.

Contribution

It redefines highly connected subgraphs based on new properties and presents a linear-time algorithm for detecting overlaps, advancing network clustering methods.

Findings

MOHCS effectively finds overlapping highly connected subgraphs.

The algorithm performs well on real and synthetic datasets.

It is computationally efficient and reliable.

Abstract

Many networks in real-life typically contain parts in which some nodes are more highly connected to each other than the other nodes of the network. The collection of such nodes are usually called clusters, communities, cohesive groups or modules. In graph terminology, it is called highly connected graph. In this paper, we first prove some properties related to highly connected graph. Based on these properties, we then redefine the highly connected subgraph which results in an algorithm that determines whether a given graph is highly connected in linear time. Then we present a computationally efficient algorithm, called MOHCS, for mining overlapping highly connected subgraphs. We have evaluated experimentally the performance of MOHCS using real and synthetic data sets from computer-generated graph and yeast protein network. Our results show that MOHCS is effective and reliable in finding overlapping highly connected subgraphs. Keywords-component; Highly connected subgraph, clustering algorithms, minimum cut, minimum degree