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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Problems solved by technology

[0006] The technical problem to be solved by the present invention is how to store the rich semantic information and structural information contained in the heterogeneous network graph in the vecto

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  • Heterogeneous network node representation learning method based on meta-path
  • Heterogeneous network node representation learning method based on meta-path
  • Heterogeneous network node representation learning method based on meta-path

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[0073] Example

[0074] The above method is described in more detail through an embodiment below.

[0075] figure 1 As shown, the meta-path-based heterogeneous network node representation learning method of the present invention includes the following steps:

[0076] A: Construct a network architecture based on the heterogeneous information network diagram; then obtain multiple different types of meta-paths from the heterogeneous information network according to the network architecture; then mathematically quantify the meta-paths to obtain the matrix representation corresponding to each meta-path.

[0077] Specifically, step A is implemented by performing the following steps:

[0078] A1: Construct a heterogeneous information network data set, crawl movie description information, user comment data and a divided movie style system from existing movie review websites. Integrate the crawled data into a heterogeneous information network graph about movies, which contains multiple types o...

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Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of graph representation learning, and more specifically, relates to a meta-path-based heterogeneous network node representation learning method. Background technique [0002] Today, network diagrams are a common form of data organization. More and more applications in the real world store and present data in the form of network graphs. For example, the social network graph formed by Facebook and twitter, the paper citation network built on DBLP, the network formed between protein molecules in biology, etc. In order to mine the valuable information hidden in the network graph, it is necessary to convert the network graph into a form that can be processed by machine learning, so network representation learning becomes a key step. [0003] Compared with homogeneous network graphs containing only one node type and relationship, heterogeneous network graphs usually contain multiple types of nodes and relationsh...

Claims

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Application Information

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IPC IPC(8): G06F16/2458
Inventor 王晓玲吴桐
Owner EAST CHINA NORMAL UNIVERSITY
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