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Community detection in networks: Structural communities versus ground truth.

Darko Hric1, Richard K Darst1, Santo Fortunato1

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Structural community detection algorithms often fail to identify groups based on node properties. This study reveals a significant divergence between structural communities and metadata groups in large networks, challenging current models.

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Area of Science:

  • Network science
  • Data analysis
  • Computational social science

Background:

  • Community detection algorithms typically use network structure to find cohesive node groups.
  • A common assumption is that these structural communities align with nodes' non-topological properties or functions.
  • Verification of this assumption has been hindered by a lack of network datasets with node classification information.

Purpose of the Study:

  • To investigate whether traditional community detection methods can identify groups defined by node metadata.
  • To assess the relationship between structurally derived communities and externally defined groups (metadata groups) in large networks.

Main Methods:

  • Evaluation of established community detection algorithms on diverse network datasets.
  • Comparison of algorithm-identified communities against known node classifications (metadata groups).

Main Results:

  • Traditional community detection methods frequently fail to recover metadata-defined groups in large-scale networks.
  • A distinct separation was observed between communities identified purely by network topology and groups defined by node attributes.
  • These findings align with recent research highlighting discrepancies between structural and functional network partitions.

Conclusions:

  • Current models of community structure may require significant revision to account for the observed separation.
  • Metadata groups might not be fully recoverable from network topology alone, suggesting limitations in purely structural approaches.
  • Future research should explore hybrid methods or alternative network representations to bridge structural and functional community information.