Message Passing Neural Networks for Hypergraphs
This work addresses the need for better neural network models for hypergraphs, which are more efficient for data with multi-object relations, though it appears incremental as it builds on existing hypergraph models.
The authors tackled the problem of processing hypergraph-structured data by proposing a new graph neural network based on message passing, showing its effectiveness with state-of-the-art results on a node classification benchmark dataset.
Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design space for neural network models for hypergraphs, thus generalizing existing models for hypergraphs. We report experiments on a benchmark dataset for node classification, highlighting the effectiveness of the proposed model with respect to other state-of-the-art methods for graphs and hypergraphs. We also discuss the benefits of using hypergraph representations and, at the same time, highlight the limitation of using equivalent graph representations when the underlying problem has relations among more than two objects.