A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
This work addresses hypergraph learning tasks for researchers and practitioners, offering a flexible framework with significant performance improvements, though it appears incremental as it builds on existing neural network methods for graphs.
The authors tackled the problem of hypergraph representation learning by introducing Hypergraph Neural Networks (HNN), which learns hyperedge-dependent node embeddings, resulting in a mean gain of 7.72% for hyperedge prediction and 11.37% for node classification over baselines.
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.