Processing apparatus, data processing method thereof, and method of training graph convolutional network (gcn) model

A processing apparatus comprising:

• a first buffer configured to store supernode information of a compressed graph, wherein the compressed graph includes a supernode corresponding to a plurality of nodes of a graph;
• a second buffer configured to store embedding information of a first node of the graph;
• and
• an operation circuit configured to obtain the supernode information from the first buffer, obtain the embedding information from the second buffer, obtain connection information between the first node and a second node of the graph, and generate modified embedding information of the first node based on the supernode information and the embedding information using a graph convolutional network (GCN) model.

A method of training a graph convolutional network (GCN) model, the method comprising:

• compressing a graph to obtain a compressed graph, wherein the compressed graph comprises a supernode representing a plurality of nodes of the graph and a superedge representing a plurality of edges of the graph;
• performing aggregation based on the supernode and the superedge of the compressed graph;
• and
• correcting a result of the aggregation based on correction information, wherein the correction information indicates a difference between a connection relationship of the compressed graph and a connection relationship of the graph.

A method comprising:

• obtaining embedding information for a node of a graph;
• compressing a graph to obtain a compressed graph, wherein the graph is compressed by grouping a plurality of nodes of the graph to form a supernode of the compressed graph;
• and
• generating, using a graph convolutional network (GCN) model, modified embedding information for the node based on the embedding information and the compressed graph.