An Investigation into Using Unsupervised Metrics to Optimise GNNs for Node Clustering

Graph Neural Networks (GNNs) can be trained to detect communities within a graph by learning from the duality of feature and connectivity information. Currently, the common approach for optimisation of GNNs is to use comparisons to ground-truth for hyperparameter tuning and model selection. In this work, we show that nodes can be clustered into communities with GNNs by solely optimising for modularity, without any comparison to ground-truth. Although modularity is a graph partitioning quality metric, we show that this can …

clustering communities connectivity cs.ai cs.lg feature gnns graph graph neural networks ground-truth hyperparameter information investigation metrics model selection networks neural networks node optimisation show truth unsupervised work