Graph width learning classification method and system based on global sampling sub-graphs

Abstract

A graph width learning classification method based on global sampling sub-graphs comprises the following steps: 1) global sampling: performing global sampling on an original network by using a connected edge sampling method to obtain sub-graphs; 2) sub-graph mapping: mapping the sub-graphs once or twice through a mapping mechanism from connecting edges to nodes to obtain a first-order network and a second-order network; 3) graph feature extraction and fusion: extracting features of an original network and all mapped networks through a Graph2vec model, and then splicing the extracted original network features and first-order and second-order network features after each sampling mapping as feature representation of an original graph; and 4) classifying a width network. The invention further discloses an efficient and accurate graph classification system based on the method. A weight matrix in a width network classifier is trained in a supervised manner by combining the fused graph features and the known graph labels. Finally, effective classification of the graphs is realized according to the weight matrix in the width network and the input graph features.

Description

technical field [0001] The invention relates to network science, data mining and data analysis technology, in particular to a graph width learning and classification method and system for global sampling subgraphs. Background technique [0002] In recent years, graph data has received more and more attention. In real life, social network, biological protein network, and literature citation network can all be described by graphs. The graph classification problem is a common task in graph data mining, such as the inference of protein toxicity and the prediction of chemical molecular properties. Therefore, it is of great practical significance to study graph classification problems. [0003] Subgraph is a basic component in the network, which can be used to describe the deeper information in the network. Networks composed of different subgraphs usually have distinct topological properties, so integrating subgraphs into many graph algorithms can often achieve higher algorithm...

AI Technical Summary

Problems solved by technology

This method uses a random walk strategy to obtain a local network structure, but the lack of global intrinsic information leads to a lack of classification accuracy, and the use of an extreme random tree as a classifier still needs to be enhanced in terms of classification training speed

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