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Creators/Authors contains: "Zhong, Yongjian"

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  1. ; ; ; (, Forty-second International Conference on Machine Learning)
    Subgraph neural networks have recently gained prominence for subgraph-level predictive tasks, but existing methods either use simple pooling over graph convolutional networks that fail to capture essential subgraph properties or rely on rigid subgraph definitions that limit performance; moreover, they cannot model long-range dependencies between and within subgraphs—an important limitation given real-world networks’ diverse structures. To address this, we propose the first implicit subgraph neural network that captures dependencies across subgraphs and integrates label-aware subgraph-level information, formulating implicit subgraph learning as a bilevel optimization problem and introducing a provably convergent algorithm requiring fewer gradient estimations than standard bilevel methods, achieving superior performance on real-world benchmarks. 
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    Free, publicly-accessible full text available July 13, 2026
  2. ; ; ; (, Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Neural Information Processing Systems)
    Accepted Manuscript Full Text Available