Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks Fumanal Idocin, Javier Cordon, Oscar Minárová, María Alonso Betanzos, Amparo Bustince, Humberto 2021-12-24T17:36:47Z 2021-12-24T17:36:47Z 2022-01-04
dc.description.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.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2022.265
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Soft Computing: Theory Innovations and Problem Solving Benefits
dc.subject affinity function
dc.subject aggregation function
dc.subject community detection
dc.subject modularity
dc.subject social networks
dc.title Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks
dc.type.dcmi text
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