Reach the Remote Neighbors: Dual-Encoding Transformer for Graphs

Lingbing Guo, Zequn Sun, Qiang Zhang, Huajun Chen

Keywords: konwledge graph prediction, node classification, graph property prediction, MSA Transformer

Abstract: Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs. Computing node-to-node attentions is infeasible on complicated graphs, e.g., knowledge graphs. One solution is to consider only the near neighbors, which, however, will lose the key merit of Transformer that attends to the elements at any distance. In this paper, we propose a new Transformer architecture, named dual-encoding Transformer (DET), which has a structural encoder to aggregate information from near neighbors and a semantic encoder to focus on useful semantically close neighbors. The two encoders can be incorporated to boost each other's performance. Our experiments demonstrate that DET achieves superior performance compared to the respective state-of-the-art attention-based methods in dealing with molecules, networks and knowledge graphs.

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TL;DR: A Transformer model for diverse graph representation learning tasks