Information dynamics algorithm for detecting communities in networks

TL;DR

This paper introduces a novel community detection algorithm inspired by social and psychological behaviors, incorporating information diffusion, memory effects, and non-linear processing to improve detection of overlapping communities and individual perspectives.

Contribution

The paper presents a new community detection method based on a modified Markov Cluster algorithm that models nodes as decision-making agents with memory, enhancing detection capabilities.

Findings

Effective in detecting overlapping communities

Able to identify communities from individual node perspectives

Allows fine-tuning of community detectability using prior knowledge

Abstract

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.