PROCESSING APPARATUS, DATA PROCESSING METHOD THEREOF, AND METHOD OF TRAINING GRAPH CONVOLUTIONAL NETWORK (GCN) MODEL

Abstract: embodiments of the present disclosure describe a processing apparatus that may obtain embedding information of a first node to be added to a graph and connection information between the graph and the first node, receive, supernode information for a supernode of a compressed graph corresponding to the graph, wherein the supernode includes a plurality of nodes from the graph, and generate modified embedding information of the first node based on the supernode information, the initial embedding information, and the connection information using a graph convolutional network (gcn) model.

Inventor(s): YEONGON CHO, YUNKI HAN, LEE-SUP KIM

CPC Classification: G06N3/0495 (Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs)

Search for rejections for patent application number 20250165759