Unsupervised Optimisation of GNNs for Node Clustering
This work addresses the challenge of unsupervised model selection for GNNs in community detection, offering a practical solution for scenarios lacking labeled data, though it is incremental by building on existing modularity-based approaches.
The paper tackled the problem of optimizing Graph Neural Networks (GNNs) for node clustering without ground-truth data by using modularity as an unsupervised metric, achieving performance comparable to supervised methods on real-world datasets. It also identified limitations where GNNs fail when feature and connectivity signals conflict in synthetic graphs.
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 be used to optimise GNNs that also encode features without a drop in performance. We take it a step further and also study whether the unsupervised metric performance can predict ground-truth performance. To investigate why modularity can be used to optimise GNNs, we design synthetic experiments that show the limitations of this approach. The synthetic graphs are created to highlight current capabilities in distinct, random and zero information space partitions in attributed graphs. We conclude that modularity can be used for hyperparameter optimisation and model selection on real-world datasets as well as being a suitable proxy for predicting ground-truth performance, however, GNNs fail to balance the information duality when the spaces contain conflicting signals.