Shuhao Shi

Representation Learning across Feature and Topology Views with Output Correction for Graph Convolutional Networks

In Graph Convolutional Networks (GCNs), the aggregation of node features in graph convolutional learning is typically guided solely by the topology of the graphs. However, both network topology and node features provide unique and valuable information. Relying solely on topology cannot yield entirel…