Multi-Granular Attention based Heterogeneous Hypergraph Neural Network

TL;DR

This paper introduces MGA-HHN, a novel heterogeneous hypergraph neural network that captures high-order relations and semantic diversity using multi-granular attention, improving node representations over existing methods.

Contribution

It proposes a new hypergraph construction method and a multi-granular attention mechanism to better model high-order relations and semantic diversity in heterogeneous graphs.

Findings

Outperforms state-of-the-art models on benchmark datasets

Enhances node classification, clustering, and visualization results

Effectively mitigates over-squashing and long-range message distortion

Abstract

Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the prevailing HeteGNNs follow the neighborhood aggregation paradigm, leveraging meta-path based message passing to learn latent node representations. However, due to the pairwise nature of meta-paths, these models fail to capture high-order relations among nodes, resulting in suboptimal performance. Additionally, the challenge of ``over-squashing'', where long-range message passing in HeteGNNs leads to severe information distortion, further limits the efficacy of these models. To address these limitations, this paper proposes MGA-HHN, a Multi-Granular Attention based Heterogeneous Hypergraph Neural Network for heterogeneous graph representation learning. MGA-HHN introduces two key innovations: (1) a novel approach for constructing meta-path based heterogeneous hypergraphs that explicitly models higher-order semantic information in heterogeneous graphs through multiple views, and (2) a multi-granular attention mechanism that operates at both the node and hyperedge levels. This mechanism enables the model to capture fine-grained interactions among nodes sharing the same semantic context within a hyperedge type, while preserving the diversity of semantics across different hyperedge types. As such, MGA-HHN effectively mitigates long-range message distortion and generates more expressive node representations. Extensive experiments on real-world benchmark datasets demonstrate that MGA-HHN outperforms state-of-the-art models, showcasing its effectiveness in node classification, node clustering and visualization tasks.