Communication Dans Un Congrès Année : 2025

Multi-Relational Community Detection in Social Platforms using Graph Neural Networks

Résumé

We propose a method to detect communities in multi-relational networks, based on a graph neural network pipeline. The method allows to target areas where communities are consensual over the different modes of the network, which are processed as different networks in the pipeline. This is done by combining the outcomes of multiple simple Graph Neural Networks, applied on each of the graphs representing different forms of interactions between users of the social platform. The method is validated on a synthetic benchmark, as a first step for further improvements. In particular, the flexible architecture of the pipeline allows to swap its subparts and create variants of community detection.

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Dates et versions

hal-05157988 , version 1 (11-07-2025)

Identifiants

Citer

Nouamane Arhachoui, Vincent Gauthier, Anastasios Giovanidis, Lionel Tabourier. Multi-Relational Community Detection in Social Platforms using Graph Neural Networks. The Fifth France's International Conference on Complex Systems (FRCCS 2025), May 2025, Bordeaux, France. pp.112-123, ⟨10.1007/978-3-032-00206-8_11⟩. ⟨hal-05157988⟩
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