  (1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised
community detection of clustered nodes is attributed to their capacity to
encode both the connectivity and feature information spaces of graphs. The
identification of latent communities holds practical significance in various
domains, from social networks to genomics. Current real-world performance
benchmarks are perplexing due to the multitude of decisions influencing GNN
evaluations for this task. (2) Three metrics are compared to assess the
consistency of algorithm rankings in the presence of randomness. The
consistency and quality of performance between the results under a
hyperparameter optimisation with the default hyperparameters is evaluated. (3)
The results compare hyperparameter optimisation with default hyperparameters,
revealing a significant performance loss when neglecting hyperparameter
investigation. A comparison of metrics indicates that ties in ranks can
substantially alter the quantification of randomness. (4) Ensuring adherence to
the same evaluation criteria may result in notable differences in the reported
performance of methods for this task. The $W$ Randomness coefficient, based on
the Wasserstein distance, is identified as providing the most robust assessment
of randomness.
