Variational co-embedding learning for attributed network clustering

Recent developments in attributed network clustering combine graph neural networks and autoencoders for unsupervised learning. Although effective, these techniques suffer from either (a) clustering-unfriendly embedding spaces or (b) limited utilization of attribute information. To address these issues, we propose a novel model called Variational Co-embedding Learning Model for Attributed Network Clustering (VCLANC), which utilizes much deeper information from the network by reconstructing both the network structure and the node attributes to perform self-supervised learning. Technically, VCLANC consists of dual variational autoencoders that co-embed nodes and attributes into the same latent space, along with a trainable Gaussian mixture prior that simultaneously performs representation learning and node clustering. To optimize the variational autoencoders and infer the latent variables of embeddings and clustering assignments, we derive a new variational lower bound that maximizes the joint likelihood of the observed network structure and node attributes. Furthermore, we also adopt a mutual distance loss on the cluster centers and a clustering assignment hardening loss on the node embeddings to strengthen clustering quality. Our experimental results on four real-world datasets demonstrate the outstanding performance of VCLANC for attributed network clustering.

Automated summary

Recent developments in attributed network clustering have combined graph neural networks and autoencoders for unsupervised learning. However, these techniques suffer from either clustering-unfriendly embedding spaces or limited utilization of attribute information. To address these issues, the proposed model, VCLANC, utilizes deeper information by reconstructing the network structure and node attributes for self-supervised learning. Experimental results on four real-world datasets demonstrate the outstanding performance of VCLANC for attributed network clustering.