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[02073] Graph convolutional networks for graph signal processing

  • Session Time & Room : 1C (Aug.21, 13:20-15:00) @F310
  • Type : Contributed Talk
  • Abstract : We propose novel graph convolution models for analyzing graph-structured time series data. Graph convolutional networks (GCNs) is a generalization of convolutional neural networks from regular grid data to irregular graph data. The major building block of a GCN is the filter. Graph filters are designed for graph convolution in spatial and spectral domains. We also propose novel graph wavelet transform methods to be jointly used with graph convolution filters, which can further improve the results.
  • Classification : 42BXX, Machine learning, graph signal processing
  • Format : Talk at Waseda University
  • Author(s) :
    • Jia He (Illinois Institute of Technology)
    • Maggie Cheng (Illinois Institute of Technology)