Network representation learning method

A learning method and network representation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of not fully utilizing the text information of nodes, achieve good scope of application, scalability, and good practicality performance and improve the classification accuracy

Active Publication Date: 2017-06-27
TSINGHUA UNIV
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is how to combine the network structure information and text information of nodes in the network to learn high-quality representations of nodes, so as to overcome the problem that existing network representation methods fail to make full use of the text information of nodes

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Embodiment Construction

[0055] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.

[0056] A network representation learning method, such as figure 1 As shown, the method includes the following steps:

[0057] S1. Establishing a plurality of first network node representation vectors based on the network structure, wherein each network node corresponds to one of the first network node representation vectors;

[0058] S2. Based on the text information of network nodes, respectively establish the first text coding model based on continuous bag of words and the second text coding model based on convolutional neural network, and use the first text coding model and the second text coding model The text encoding model establishes a plurality of second network node representation vectors, wherein ...

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Abstract

The invention provides a network representation learning method. Learning is performed by comprehensively considering text information and a network structure; for the text information part, different types of continuous bag-of-words-based and convolutional neural network-based text encoding models are designed; the network structure information of nodes in a network is used for predicting neighbor nodes of current nodes, and the text information of the nodes are used for predicting representation vectors of the text information of the current nodes; and by use of the method, the text information and the network structure information of the nodes can be effectively encoded in the representation vectors, and the classification accuracy is remarkably improved in a node classification task. Meanwhile, the method fully considers effective information such as the text information in a practical network, achieves an excellent effect in different types of information network data, and has high practicality.

Description

technical field [0001] The invention belongs to the technical field of natural language processing and representation learning, and more specifically relates to a network representation learning method. Background technique [0002] Entities in the real world usually interact with each other to form large-scale complex networks. Research on network analysis has made tremendous progress in recent years, from sociology to computational science. Traditional network analysis techniques regard each network node as a unique symbol. This representation method usually faces the problem of sparsity, which greatly affects the final effect for many tasks, such as node classification, personalized recommendation, anomaly detection and relationship prediction. [0003] In order to overcome the sparsity problem, inspired by representation learning in recent years, scholars have proposed a network representation learning method for network analysis. The purpose of network representation...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/22
CPCG06F40/12G06F40/16
Inventor 孙茂松涂存超刘知远栾焕博刘奕群马少平
Owner TSINGHUA UNIV
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