Event Prediction Based On Large Scale Network Subgraph Convolution

Aim: ing at the problem of event prediction in large-scale event network, a collapse subgraph convolution (CSGCN) algorithm is proposed, which uses event subgraph to predict the subsequent events of event group. CSGCN algorithm collapses the edge induced event subgraph in large-scale event network, removes the irrelevant event nodes from the subgraph, and forms a new event subgraph. GCN algorithm is used to learn the graph embedding representation of the event subgraph, and the subsequent events of the event group are predicted by comparing the similarity between the graph embedding representation of the event group and the subsequent events. Because only some related nodes are processed each time, the application of the model in large-scale data graph is feasible. Through experiments, we explore and verify the effectiveness of extracting features from subgraphs of large-scale graph by using graph convolution training to obtain graph embedding representation. We find that GCN has better event prediction effect than Euclidean distance and co rotation similarity, which further shows that graph convolution algorithm has good performance in the field of graph feature extraction.