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Creators/Authors contains: "Ye, Ze"

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  1. (, Proceedings of the 8th International Conference on Learning Representations (ICLR 2020))
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)
    Local structural information can increase the adaptability of graph convolutional networks to large graphs with heterogeneous topology. Existing methods only use relatively simplistic topological information, such as node degrees.We present a novel approach leveraging advanced topological information, i.e., persistent homology, which measures the information flow efficiency at different parts of the graph. To fully exploit such structural information in real world graphs, we propose a new network architecture which learns to use persistent homology information to reweight messages passed between graph nodes during convolution. For node classification tasks, our network outperforms existing ones on a broad spectrum of graph benchmarks. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS))
    Local structural information can increase the adaptability of graph convolutional networks to large graphs with heterogeneous topology. Existing methods only use relatively simplistic topological information, such as node degrees.We present a novel approach leveraging advanced topological information, i.e., persistent homology, which measures the information flow efficiency at different parts of the graph. To fully exploit such structural information in real world graphs, we propose a new network architecture which learns to use persistent homology information to reweight messages passed between graph nodes during convolution. For node classification tasks, our network outperforms existing ones on a broad spectrum of graph benchmarks. 
    more » « less
    Accepted Manuscript Full Text Available
  4. ; ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, 8th International Conference on Learning Representations (ICLR))
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI))
    null (Ed.)
    Full Text Available 
  7. ; ; ; (, The 9th International Conference on Probabilistic Graphical Models (PGM))
    Accepted Manuscript Full Text Available