Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
This addresses the challenge of scarce supervised signals in graph learning, offering an unsupervised alternative for domain-specific applications like graph classification and regression.
The authors tackled the problem of learning hierarchical graph abstractions without supervision by proposing OTCoarsening, which uses optimal transport to parameterize coarsening and transport cost matrices, resulting in competitive performance for graph classification and regression compared to supervised methods.
Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies were developed based on mathematically driven heuristics. Recently, resurgent interests exist in deep learning to design hierarchical methods learnable through differentiable parameterization. These approaches are paired with downstream tasks for supervised learning. In practice, however, supervised signals (e.g., labels) are scarce and are often laborious to obtain. In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs. We demonstrate that the proposed approach produces meaningful coarse graphs and yields competitive performance compared with supervised methods for graph classification and regression.