A nonlinear diffusion method for semisupervised learning on hypergraphs
Abstract
Hypergraphs are a common model for multiway relationships in data, and hypergraph semisupervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes. Diffusions and label spreading are classical techniques for semisupervised learning in the graph setting, and there are some standard ways to extend them to hypergraphs. However, these methods are linear models, and do not offer an obvious way of incorporating node features for making predictions. Here, we develop a nonlinear diffusion process on hypergraphs that spreads both features and labels following the hypergraph structure, which can be interpreted as a hypergraph equilibrium network. Even though the process is nonlinear, we show global convergence to a unique limiting point for a broad class of nonlinearities, which is the global optimum of a interpretable, regularized semisupervised learning loss function. The limiting point serves as a node embedding from which we make predictions with a linear model. Our approach is much more accurate than several hypergraph neural networks, and also takes less time to train.
 Publication:

arXiv eprints
 Pub Date:
 March 2021
 arXiv:
 arXiv:2103.14867
 Bibcode:
 2021arXiv210314867T
 Keywords:

 Computer Science  Machine Learning;
 Computer Science  Social and Information Networks;
 Physics  Data Analysis;
 Statistics and Probability