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

Fumanal Idocin, Javier
Cordon, Oscar
Minárová, María
Alonso Betanzos, Amparo
Bustince, Humberto
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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.
Soft Computing: Theory Innovations and Problem Solving Benefits, affinity function, aggregation function, community detection, modularity, social networks
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