  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.
