Improving Community Detection by Mining Social Interactions

TL;DR

This paper proposes a method that leverages temporal features to filter out random social interactions, enhancing the accuracy of community detection in social networks.

Contribution

It introduces a novel process that exploits temporal features to remove noise from social network data, improving community detection results.

Findings

Removing random interactions leads to more modular communities

Temporal analysis refines social relationship data

Enhanced community detection accuracy

Abstract

Social relationships can be divided into different classes based on the regularity with which they occur and the similarity among them. Thus, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this context, in this paper we propose a process to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random interactions, we observe that social networks converge to a topology with more purely social relationships and more modular communities.