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Multi-relational Community Detection in Social Platforms Using Graph Neural Networks

  • Sorbonne Université

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Pages112-123
Number of pages12
DOIs
Publication statusPublished - 1 Jan 2025

Publication series

NameStudies in Systems, Decision and Control
Volume615
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Keywords

  • community detection
  • graph neural networks
  • multi-relational networks
  • online social platforms

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