While graph neural networks (GNNs) have achieved notable success in various graph mining tasks, conventional GNNs only consider the pairwise correlation in 1-hop neighbors without considering the long-term relations and the high-order patterns, thus limiting their performances. Therefore, several previous works explore the motif, i.e., the frequent high-order subgraphs, to exploit the high-order structure on the graph. However, motif-based methods usually require an unacceptable computational time to enumerate all possible combinations on the graph. As a promising method to discover the graph structure, we introduce a new GNN framework, namely Random Walk Conformer (RWC), to model both global correlations and local patterns from motifs based on the random walk. Besides, we propose random walk encoding to help RWC capture topological information, which is proven to be more expressive than conventional spatial encoding. Extensive experiment results manifest that RWC achieves state-of-the-art performance on graph classification and regression tasks.

Random Walk Conformer: Learning Graph Representation from Long and Short Range

ML: Graph-based Machine Learning 5

poster

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Pei-Kai Yeh

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Random Walk Conformer: Learning Graph Representation from Long and Short Range

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