Classification method based on hyper-graph transformation network

Abstract

The invention provides a classification method based on a hyper-graph transformation network, which is used for solving the problem that in the prior art, high-order semantic information in a heterogeneous network cannot be deeply explored, so that classification cannot be accurately carried out. According to the method, an end-to-end hyper-graph transform network (HGTN) is provided, the communication ability between hyper-edge amplification nodes is used for learning a high-order relation, and semantic information between different types of nodes is mined. Specifically, an attention mechanism is utilized to distribute weights for different types of hyper-graphs, high-order semantic information implied in an original heterogeneous hyper-graph is subjected to cascade learning, useful meta-paths are generated, node embedding features are learned in an end-to-end mode, and a node classification task is completed. The method has good accuracy and universality, and is suitable for node classification tasks of heterogeneous networks such as citation networks, media networks, social networks and the like.

Description

technical field [0001] The invention mainly relates to the fields of graph mining, deep neural network algorithm and node division, and in particular is a classification method based on a hypergraph transformation network. Background technique [0002] In recent years, the application of deep networks to non-Euclidean data processing tasks has attracted considerable attention. Among them, the emergence of the graph neural network has made a major breakthrough in the above tasks. ordinary Figure 1 An edge can connect two vertices, representing the relationship and message propagation between paired nodes. But for complex many-to-many relationships, simple graphs will lose a lot of useful high-level information. For example, papers are used as nodes, and the condition for connecting nodes is that they belong to the same author. For simple graphs, if two papers are authored by the same person, a connection relationship is established. However, the reality is not so simple:...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problem that the existing technology cannot deeply explore the high-order node information of heterogeneous networks, and thus cannot accurately classify, the present invention provides a classification method for hypergraph transformation neural networks

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