Social Network Community Detection by Combining Self-Organizing Maps and Genetic Algorithms

Mehdi Ellouze,Ning Cai

doi:10.1155/2021/6699130

Abstract

Social networks have become an important source of information from which we can extract valuable indicators that can be used in many fields such as marketing, statistics, and advertising among others. To this end, many research works in the literature offer users some tools that can help them take advantage of this mine of information. Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. The novelty of this approach is that we do not directly tackle the social networks to find these communities. We rather proceeded in two stages; first, we detected community cores through a special type of self-organizing map called the Growing Hierarchical Self-Organizing Map (GHSOM). In the second stage, the agglomerations resulting from GHSOM were grouped to retrieve the final communities. The quality of the final partition would be under the control of an evaluation function that is maximized through genetic algorithms. Our system was tested on real and artificial databases, and the obtained results are really encouraging.

Highlights

Social networks involve such a wealth that various people from different fields try to exploit for valuable information
The agglomerations resulting from Growing Hierarchical Self-Organizing Map (GHSOM) were grouped to retrieve the final communities. e quality of the final partition would be under the control of an evaluation function that is maximized through genetic algorithms
Our contributions can be summarized as follows: (i) We introduced the concept of community cores and used pattern recognition techniques, represented in Growing Hierarchical Self-Organizing Maps (GHSOM) to detect them

Summary

Introduction

Social networks involve such a wealth that various people from different fields try to exploit for valuable information. Detecting communities in a social network is a complex task because nothing is known about the structure of the communities, their size, their core nodes, and so on. Ey represent the cores of the final communities Once detected, these agglomerations will compete to attract each other to produce the eventual partition of communities. (i) We introduced the concept of community cores and used pattern recognition techniques, represented in Growing Hierarchical Self-Organizing Maps (GHSOM) to detect them. (ii) We coupled the genetic algorithms with Growing Hierarchical Self-OrganizingMaps (GHSOM) to extract the final communities. It is not a simple succession of steps. E genetic algorithm is tuned when working with the results of the Growing Hierarchical Self-Organizing Maps.

Related Works

Agglomerative Approaches

Proposed Approach

Experiments

Conclusion