A graph classification method based on graph set reconstruction and graph kernel dimensionality reduction

Abstract

The invention provides a graph classification method based on graph set reconstruction and graph kernel dimensionality reduction. The method comprises the steps of: 1) performing frequent sub-graph mining on a graph data set used for training, and performing discriminative sub-graph screening on obtained frequent sub-graphs with the emerging frequentness differences of the sub-graphs in a positive class and a negative class; 2) reconstructing the original graph set with selected discriminative frequent sub-graphs; 3) obtaining a kernel matrix for describing the similarity between every two graphs in the newly-reconstructed graph set by using a Weisfeiler-Lehman shortest path kernel method, and based on class label information of training graphs, performing dimensionality reduction on high-dimensionality kernel matrixes by using a KFDA method; 4) training graph data projected to a low-dimensionality vector space based on an extreme learning machine to build a classifier; 5) standardizing graph data requiring classification, projecting the data to a low-dimensionality space obtained through training and inputting the projected data to the classifier to obtain a classification result. The method can directly classify graph data without class labels and guarantee high classification accuracy.

Description

technical field [0001] The present invention relates to frequent subgraph mining, graph kernel mapping, classifier construction, etc., specifically relates to a graph classification method based on atlas reconstruction and graph kernel dimensionality reduction, and belongs to the technical field of machine learning and data mining. Background technique [0002] With the application of data mining in many fields such as informatics, bioinformatics, and network intrusion detection, more and more data present new features such as strong structure and complex relationship between data, such as circuits, images, compounds, and protein structures. , biological networks, etc. As a type of data structure, a graph can be used to clearly describe various things and their interrelationships. For example, in the field of bioinformatics, biologists hope to find frequently occurring substances with the same substructure as toxic substances. At this time, the molecular structure can be de...

AI Technical Summary

Problems solved by technology

Classification based on theoretical index features mostly uses feature path length, clustering coefficient, betweenness, etc. Experts in their specific fields apply their professional background knowledge to specify physical and chemical characteristics (such as molecular weight, molecular density, etc.) as the division standard. Although this can avoid over-fitting, the algorithm is simple and easy to create, but it is easy to lose structural information, and Need too strong professional knowledge, poor universality

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