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Uncertainty in GNN Learning Evaluations: A Comparison between Measures for Quantifying Randomness in GNN Community

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Graph neural networks (GNNs) excel at unsupervised community detection. Rigorous hyperparameter optimization is crucial for accurate performance evaluation, with the Wasserstein distance offering the most reliable randomness assessment.

Keywords:
benchmarkscommunity detectiongraph neural networkshyperparameter optimisationnode clusteringrepresentation learning

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Area of Science:

  • Graph Neural Networks (GNNs)
  • Machine Learning
  • Network Analysis

Background:

  • Unsupervised community detection using GNNs leverages both graph connectivity and feature information.
  • Accurate evaluation of GNN performance for community detection is complicated by numerous influencing factors.
  • Identifying latent communities has broad applications in social networks, genomics, and beyond.

Purpose of the Study:

  • To evaluate the impact of hyperparameter optimization on GNN performance in unsupervised community detection.
  • To compare the consistency and quality of algorithm rankings across different evaluation metrics.
  • To identify the most robust metric for assessing randomness in GNN performance evaluations.

Main Methods:

  • Comparison of GNN performance using hyperparameter optimization versus default hyperparameters.
  • Evaluation of three distinct metrics for assessing the consistency of algorithm rankings under randomness.
  • Assessment of the Wasserstein distance (W randomness coefficient) for quantifying randomness.

Main Results:

  • Neglecting hyperparameter optimization leads to a significant loss in GNN performance.
  • Ties in algorithm ranks can substantially affect the quantification of randomness.
  • The Wasserstein distance provides the most robust assessment of randomness compared to other metrics.

Conclusions:

  • Standardized evaluation criteria are essential for reproducible GNN performance reporting.
  • Hyperparameter tuning is critical for achieving optimal GNN performance in community detection tasks.
  • The W randomness coefficient offers a reliable method for evaluating the stability of GNN-based community detection algorithms.