Deep latent position model for node clustering in graphs

Dingge LIANG, Marco Corneli, Charles Bouveyron, Pierre Latouche

Keywords: Network analysis, Graph clustering, Unsupervised learning

Abstract: With the significant increase of interactions between individuals through numeric means, the clustering of vertex in graphs has become a fundamental approach for analysing large and complex networks. We propose here the deep latent position model (DeepLPM), an end-to-end clustering approach which combines the widely used latent position model (LPM) for network analysis with a graph convolutional network (GCN) encoding strategy. Thus, DeepLPM can automatically assign each node to its group without using any additional algorithms and better preserves the network topology. Numerical experiments on simulated data and an application on the Cora citation network are conducted to demonstrate its effectiveness and interest in performing unsupervised clustering tasks.

Dual Submission: Yes, ESANN2022 (European Symposium on Artificial Neural Networks).