Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective

Understand

In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs).

• However, the computing of GCN usually requires a large memory space for keeping the entire graph.
• In consequence, GCN is not flexible enough, especially for large scale graphs in complex real-world applications.
• Fortunately, methods based on Matrix Factorization (MF) naturally support constructing mini-batches, and thus are more friendly to distributed computing compared with GCN.