Overlapping community detection algorithms using Modularity and the cosine

TL;DR

This paper introduces two novel algorithms for overlapping community detection in networks, utilizing extended modularity and cosine functions, applicable to both directed and undirected graphs, validated through experiments on real data.

Contribution

The paper proposes two new algorithms for overlapping community detection based on extended modularity and cosine similarity, applicable to directed and undirected networks.

Findings

Algorithms effectively detect overlapping communities in real networks.

Applicable to both directed and undirected graph structures.

Validated through experiments demonstrating feasibility and effectiveness.

Abstract

The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously.This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine function. The applicability of our algorithms extends to both undirected and directed graph structures. To demonstrate the feasibility and effectiveness of these algorithms, we conducted experiments using real data.