Hierarchical Transformer for Scalable Graph Learning

• URL: http://arxiv.org/abs/2305.02866v2
• Date: Fri, 5 May 2023 05:21:18 GMT
• Title: Hierarchical Transformer for Scalable Graph Learning
• Authors: Wenhao Zhu, Tianyu Wen, Guojie Song, Xiaojun Ma, Liang Wang
• Abstract summary: Graph Transformer has demonstrated state-of-the-art performance on benchmarks for graph representation learning. The complexity of the global self-attention mechanism presents a challenge for full-batch training when applied to larger graphs. We introduce the Hierarchical Scalable Graph Transformer (HSGT) as a solution to these challenges. HSGT successfully scales the Transformer architecture to node representation learning tasks on large-scale graphs, while maintaining high performance.
• Score: 22.462712609402324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning. However, as current implementations of Graph Transformer primarily focus on learning representations of small-scale graphs, the quadratic complexity of the global self-attention mechanism presents a challenge for full-batch training when applied to larger graphs. Additionally, conventional sampling-based methods fail to capture necessary high-level contextual information, resulting in a significant loss of performance. In this paper, we introduce the Hierarchical Scalable Graph Transformer (HSGT) as a solution to these challenges. HSGT successfully scales the Transformer architecture to node representation learning tasks on large-scale graphs, while maintaining high performance. By utilizing graph hierarchies constructed through coarsening techniques, HSGT efficiently updates and stores multi-scale information in node embeddings at different levels. Together with sampling-based training methods, HSGT effectively captures and aggregates multi-level information on the hierarchical graph using only Transformer blocks. Empirical evaluations demonstrate that HSGT achieves state-of-the-art performance on large-scale benchmarks with graphs containing millions of nodes with high efficiency.

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