Commute Graph Neural Networks
<|reference_start|>Commute Graph Neural Networks: Graph Neural Networks (GNNs) have shown remarkable success in learning from graph-structured data. However, their application to directed graphs (digraphs) presents unique challenges, primarily due to the inherent asymmetry in node relationships. Traditional GNNs are adept at capturing unidirectional relations but fall short in encoding the mutual path dependencies between nodes, such as asymmetrical shortest paths typically found in digraphs. Recognizing this gap, we introduce Commute Graph Neural Networks (CGNN), an approach that seamlessly integrates node-wise commute time into the message passing scheme. The cornerstone of CGNN is an efficient method for computing commute time using a newly formulated digraph Laplacian. Commute time is then integrated into the neighborhood aggregation process, with neighbor contributions weighted according to their respective commute time to the central node in each layer. It enables CGNN to directly capture the mutual, asymmetric relationships in digraphs. Extensive experiments confirm the superior performance of CGNN.<|reference_end|>
@article{zhuo2024commute,
    title={Commute Graph Neural Networks},
    author={Wei Zhuo, Guang Tan},
    journal={arXiv preprint arXiv:2407.01635},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01635},
    primaryClass={cs.LG cs.AI}
}