Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks

TL;DR

This paper introduces novel combinations of affinity functions to enhance community detection algorithms in social networks, aiming to better capture diverse social interactions and improve detection accuracy.

Contribution

It proposes a new method of combining affinity functions to extend existing community detection algorithms for more comprehensive social interaction modeling.

Findings

Enhanced community detection performance with combined affinity functions

Ability to model diverse social mechanics in networks

Improved detection accuracy over traditional methods

Abstract

Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.