VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation

Group interactions arise in various scenarios in real-world systems: collaborations of researchers, co-purchases of products, and discussions in online Q&A sites, to name a few. Such higher-order relations are naturally modeled as hypergraphs, which consist of hyperedges (i.e., any-sized subsets of nodes). For hypergraphs, the challenge to learn node representation when features or labels are not available is imminent, given that (a) most real-world hypergraphs are not equipped with external features while (b) most existing approaches for hypergraph learning resort to additional information. Thus, in this work, we propose VilLain, a novel self-supervised hypergraph representation learning method based on the propagation of virtual labels (v-labels). Specifically, we learn for each node a sparse probability distribution over v-labels as its feature vector, and we propagate the vectors to construct the final node embeddings. Inspired by higher-order label homogeneity, which we discover in real-world hypergraphs, we design novel self-supervised loss functions for the v-labels to reproduce the higher-order structure-label pattern. We demonstrate that VilLain is: (a) Requirement-free: learning node embeddings without relying on node labels and features, (b) Versatile: giving embeddings that are not specialized to specific tasks but generalizable to diverse downstream tasks, and (c) Accurate: more accurate than its competitors for node classification, hyperedge prediction, node clustering, and node retrieval tasks. Our code and dataset are available at https://github.com/geon0325/VilLain.