5 research outputs found
DIGRAC: Digraph Clustering Based on Flow Imbalance
Node clustering is a powerful tool in the analysis of networks. We introduce
a graph neural network framework to obtain node embeddings for directed
networks in a self-supervised manner, including a novel probabilistic imbalance
loss, which can be used for network clustering. Here, we propose directed flow
imbalance measures, which are tightly related to directionality, to reveal
clusters in the network even when there is no density difference between
clusters. In contrast to standard approaches in the literature, in this paper,
directionality is not treated as a nuisance, but rather contains the main
signal. DIGRAC optimizes directed flow imbalance for clustering without
requiring label supervision, unlike existing GNN methods, and can naturally
incorporate node features, unlike existing spectral methods. Experimental
results on synthetic data, in the form of directed stochastic block models, and
real-world data at different scales, demonstrate that our method, based on flow
imbalance, attains state-of-the-art results on directed graph clustering, for a
wide range of noise and sparsity levels and graph structures and topologies.Comment: 36 pages (10 pages for main text, 3 pages for references