Heterogeneous network node representation learning method based on meta-path
A heterogeneous network and learning method technology, applied in the field of meta-path-based heterogeneous network node representation learning, can solve the problem of insufficient processing ability of complex heterogeneous network graphs, and achieve high classification accuracy
Pending Publication Date: 2019-12-10
EAST CHINA NORMAL UNIVERSITY
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The invention provides a heterogeneous network node representation learning method based on a meta-path. According to the method, various node types and relationship types contained in a heterogeneousnetwork graph are considered, and rich semantic information and structure information in the network graph are stored in a plurality of meta-paths in a meta-path extraction mode. In each meta-path, the feature information of the nodes in the meta-path is saved by learning the vector representation of the nodes, and then the plurality of meta-paths are integrated together for common training, so that the semantic information and the structure information in the whole heterogeneous network are saved in the node vector representation. The method has higher classification accuracy, the feature information of the nodes in the heterogeneous network can be better stored in the vector representation of the nodes, the meta-paths can be freely selected according to specific target tasks, and the method is more flexible.
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