Graph classification method and system fusing high-order structure embedding and composite pooling

A classification method and high-level technology, applied in the field of graph classification integrating high-order structure embedding and compound pooling, can solve the problems of not explicitly considering simultaneously, lack of attention to high-order graph structure information, and failure to consider structure information.
CN114792384APending Publication Date: 2022-07-26SHANDONG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SHANDONG UNIV
Publication Date
2022-07-26

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Abstract

The invention belongs to the technical field of artificial intelligence graph classification, and provides a graph classification method and system fusing high-order structure embedding and composite pooling, and the method comprises the steps: obtaining a to-be-classified graph; inputting a to-be-classified graph into the graph neural network to obtain a category to which the graph belongs; wherein for each sub-graph set of the graph, each convolutional layer calculates the feature of each sub-graph based on the sub-graph set output by the previous neural network layer, each composite pooling layer updates the sub-graph set based on the feature of each sub-graph output by the convolutional layer, and meanwhile, for each sub-graph in the updated sub-graph set, the feature of each composite pooling layer is calculated based on the feature of each sub-graph output by the convolutional layer. The features of the sub-graphs in the local neighborhood are fused through an attention mechanism, and the features of the sub-graphs are updated; and obtaining a graph representation vector by the reading layer, and inputting the graph representation vector into the classifier to obtain a category to which the graph belongs. A high-order structure is utilized, messages are directly transmitted among the sub-graphs, structural information invisible in node level is captured, and the classification precision of the graphs is improved.
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Description

technical field

[0001] The invention belongs to the technical field of artificial intelligence graph classification, and in particular relates to a graph classification method and system integrating high-order structure embedding and compound pooling. Background technique

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In real life, many real-world data can be naturally represented by graphs. From biological and chemical informatics to social network analysis, graph-structured data is ubiquitous in application fields, and graph classification is one of the important applications. Briefly, given a dataset of graphs of the form (G, y), where G represents a graph and y is its class, the goal of the graph classification task is to use the given graph structure and node features to predict the The label associated with the graph. Many real graphs have typical loc...

Claims

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