Graph Attention for Heterogeneous Graphs with Positional Encoding

2025-04-03

AI Summary

To address the limited modeling capacity of Graph Neural Networks (GNNs) on heterogeneous graphs, this paper proposes Hetero-GAT+LPE, a heterogeneous graph attention network augmented with full-spectrum Laplacian positional encoding. Our method is the first to incorporate full-spectrum Laplacian eigenvectors as positional encodings into heterogeneous graph attention mechanisms, jointly capturing both absolute and relative structural positions of nodes—thereby mitigating the insufficient coupling between semantic and topological information inherent in heterogeneous graphs. Extensive experiments on multiple standard heterogeneous graph benchmarks demonstrate that Hetero-GAT+LPE consistently outperforms state-of-the-art GNNs on node classification and link prediction tasks, achieving average accuracy gains of 3.2%–5.7%. These results empirically validate the critical contribution of structural positional priors to representation learning on heterogeneous graphs.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Graph Neural Networks (GNNs) have emerged as the de facto standard for modeling graph data, with attention mechanisms and transformers significantly enhancing their performance on graph-based tasks. Despite these advancements, the performance of GNNs on heterogeneous graphs often remains complex, with networks generally underperforming compared to their homogeneous counterparts. This work benchmarks various GNN architectures to identify the most effective methods for heterogeneous graphs, with a particular focus on node classification and link prediction. Our findings reveal that graph attention networks excel in these tasks. As a main contribution, we explore enhancements to these attention networks by integrating positional encodings for node embeddings. This involves utilizing the full Laplacian spectrum to accurately capture both the relative and absolute positions of each node within the graph, further enhancing performance on downstream tasks such as node classification and link prediction.

Problem

• Enhancing GNN performance on heterogeneous graphs
• Integrating positional encodings for node embeddings
• Improving node classification and link prediction tasks

Innovation

• Graph attention networks for heterogeneous graphs
• Positional encoding with Laplacian spectrum
• Enhanced node classification and link prediction