Graph Neural Networks (GNNs) have gained popularity as a neural network-based approach for handling graph-structured data, leveraging their capacity to merge two information sources through the propagation and aggregation of node feature encodings along the network's connectivity [17]. Nodes within a network can be organized into communities based on similarities in associated features and/or edge density [33]. Analyzing the network structure to identify clusters or communities of nodes proves valuable in addressing real-world issues like misinformation detection [25], genomic feature discovery [4], and social network or research recommendation [49]. We consider unsupervised neural approaches to community detection that do not use any ground truth or labels during training to optimise the loss functions. As an unsupervised task, the identification of node clusters relies on latent patterns within the dataset rather than on "ground-truth" labels.

Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due to their ability to encode the dual dimensionality of the connectivity and feature information spaces of graphs. Identifying the latent communities has many practical applications from social networks to genomics. Current benchmarks of real world performance are confusing due to the variety of decisions influencing the evaluation of GNNs at this task. To address this, we propose a framework to establish a common evaluation protocol. We motivate and justify it by demonstrating the differences with and without the protocol. The W Randomness Coefficient is a metric proposed for assessing the consistency of algorithm rankings to quantify the reliability of results under the presence of randomness. We find that by ensuring the same evaluation criteria is followed, there may be significant differences from the reported performance of methods at this task, but a more complete evaluation and comparison of methods is possible.