Papers › Towards Dynamic Message Passing on Graphs

Towards Dynamic Message Passing on Graphs

31 Oct 2024arXiv:2410.23686archive 2025-07-28

Junshu Sun, Chenxue Yang, Xiangyang Ji, Qingming Huang, Shuhui Wang

Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message passing and restricts the ability of GNNs. Despite efforts to mitigate the reliance, existing study encounters message-passing bottlenecks or high computational expense problems, which invokes the demands for flexible message passing with low complexity. In this paper, we propose a novel dynamic message-passing mechanism for GNNs. It projects graph nodes and learnable pseudo nodes into a common space with measurable spatial relations between them. With nodes moving in the space, their evolving relations facilitate flexible pathway construction for a dynamic message-passing process. Associating pseudo nodes to input graphs with their measured relations, graph nodes can communicate with each other intermediately through pseudo nodes under linear complexity. We further develop a GNN model named 𝐍² based on our dynamic message-passing mechanism. 𝐍² employs a single recurrent layer to recursively generate the displacements of nodes and construct optimal dynamic pathways. Evaluation on eighteen benchmarks demonstrates the superior performance of 𝐍² over popular GNNs. 𝐍² successfully scales to large-scale benchmarks and requires significantly fewer parameters for graph classification with the shared recurrent layer.

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