Unsupervised Community Detection with Modularity-Based Attention Model
arXiv:1905.10350
Abstract
In this paper we take a problem of unsupervised nodes clustering on graphs and show how recent advances in attention models can be applied successfully in a "hard" regime of the problem. We propose an unsupervised algorithm that encodes Bethe Hessian embeddings by optimizing soft modularity loss and argue that our model is competitive to both classical and Graph Neural Network (GNN) models while it can be trained on a single graph.
References in corpus (6)
- Fast unfolding of communities in large networks
- Finding community structure in very large networks
- Spectral methods for network community detection and graph partitioning
- Phase transition in the detection of modules in sparse networks
- An efficient and principled method for detecting communities in networks
- Attention, Learn to Solve Routing Problems!