Adaptive Distance Message Passing From the Multi-Relational Edge ViewDownload PDF

01 Mar 2023 (modified: 31 May 2023)Submitted to Tiny Papers @ ICLR 2023Readers: Everyone
Keywords: Message passing, graph representation learning, multi-relations
TL;DR: Message of edges can be passed to nodes at different distances
Abstract: Message-passing graph neural networks (MP-GNNs) excel in deep learning on graphs. Despite their success in various studies, they are limited by passing information to the fixed length $k$ distance neighbouring nodes, where $k$ is the number of layers. In reality, different types of edges (alternatively relations) may influence nodes at varying distance and should not be uniformly treated. This paper proposes an adaptive distance message-passing method that considers the unique roles of edge types, addressing this issue. Experiments on real-world datasets validate the effectiveness of our approach.
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