A Regularized Wasserstein Framework for Graph Kernels
2021 IEEE International Conference on Data Mining (ICDM)(2021)
摘要
We propose a learning framework for graph kernels, which is theoretically grounded on regularizing optimal transport. This framework provides a novel optimal transport distance metric, namely Regularized Wasserstein (RW) discrepancy, which can preserve both features and structure of graphs via Wasserstein distances on features and their local variations, local barycenters and global connectivity. ...
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关键词
Measurement,Databases,Conferences,Benchmark testing,Data models,Data mining,Kernel
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