A Tutorial on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide

Higher-order interactions (HOIs) are ubiquitous in real-world networks, such as group discussions on online Q&A platforms, co-purchases of items in e-commerce, and collaborations of researchers. Investigation of deep learning for networks of HOIs, expressed as hypergraphs, has become an important agenda for the data mining and machine learning communities. As a result, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given this emerging trend, we provide a timely tutorial dedicated to HNNs. We cover the following topics: (1) inputs, (2) message passing schemes, (3) training strategies, (4) applications (e.g., recommender systems and time series analysis), and (5) open problems of HNNs. This tutorial is intended for researchers and practitioners who are interested in hypergraph representation learning and its applications.