[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.