Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network
This work provides an integrated, unsupervised method for community detection, which is a fundamental problem for researchers working with graph data.
This paper introduces a novel self-expressive graph neural network for unsupervised community detection, addressing the sub-optimality of decoupled GNN training and subsequent clustering. The proposed end-to-end solution achieves state-of-the-art performance on multiple public datasets.
Graph neural networks (GNNs) are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction. Comparatively lesser work has been done to design GNNs which can operate directly for community detection on graphs. Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities. However, such decoupled approaches are inherently sub-optimal. Designing an unsupervised loss function to train a GNN and extract communities in an integrated manner is a fundamental challenge. To tackle this problem, we combine the principle of self-expressiveness with the framework of self-supervised graph neural network for unsupervised community detection for the first time in literature. Our solution is trained in an end-to-end fashion and achieves state-of-the-art community detection performance on multiple publicly available datasets.