Graph Attention for Heterogeneous Graphs with Positional Encoding
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916673508868096 |
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| author | Nayak, Nikhil Shivakumar |
| author_facet | Nayak, Nikhil Shivakumar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_02938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Attention for Heterogeneous Graphs with Positional Encoding Nayak, Nikhil Shivakumar Machine Learning Artificial Intelligence Discrete Mathematics Differential Geometry 53-02 G.2.2; I.2.0; I.2.4; G.3 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. |
| title | Graph Attention for Heterogeneous Graphs with Positional Encoding |
| topic | Machine Learning Artificial Intelligence Discrete Mathematics Differential Geometry 53-02 G.2.2; I.2.0; I.2.4; G.3 |
| url | https://arxiv.org/abs/2504.02938 |