Search for the UGLE Truth: An Investigation into Unsupervised GNN Learning Environments

Graph Neural Networks (GNNs) are a pertinent tool for any machine learning task due to their ability to learn functions over graph structures, a powerful and expressive data representation. The detection of communities, an unsupervised task has increasingly been performed with GNNs. Clustering nodes in a graph using the multi-dimensionality of node features with the connectivity of the graph has many applications to real world tasks from social networks to genomics. Unfortunately, there is currently a gap in the literature with no established sufficient benchmarking environment for fairly and rigorously evaluating GNN based community detection, thereby potentially impeding progress in this nascent field. We observe the particular difficulties in this setting is the ambiguous hyperparameter tuning environments combined with conflicting metrics of performance and evaluation datasets. In this work, we propose and evaluate frameworks for the consistent comparisons of community detection algorithms using GNNs. With this, we show the strong dependence of the performance to the experimental settings, exacerbated by factors such as the use of GNNs and the unsupervised nature of the task, providing clear motivation for the use of a framework to facilitate congruent research in the field.