A network representation learning method based on multi-order proximity similarity

A similarity and network technology, applied in the field of complex network analysis, to achieve the effect of rich semantics, high reliability and authenticity

Active Publication Date: 2019-03-08
BEIJING UNIV OF POSTS & TELECOMM
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the above methods are only based on the first-order similarity or second-order similarity between nodes, and there are few cases where higher-order similarity is discussed.

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  • A network representation learning method based on multi-order proximity similarity
  • A network representation learning method based on multi-order proximity similarity
  • A network representation learning method based on multi-order proximity similarity

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Embodiment Construction

[0041] Such as figure 1 As shown, the present invention discloses a network representation learning method based on multi-order proximity similarity, including:

[0042] (1) The real social network structure is abstracted into an undirected graph G(V,E), where V represents a user node, and E represents the follow-to-follow relationship between users.

[0043] (2) Take out a node A in the network, find out the adjacent nodes whose step size does not exceed k, and put these nodes into A's context node set S A middle. Each node has a corresponding set of context nodes in the following form: S A ={[B:NN AB ],[C:NN AC ],....,[Q:NN AQ ]},

[0044] Among them, [] represents the context node element, and the nodes in the element exist in the form of key-value pairs. The key of the element represents the name of the context node, and the value of the element is the degree of association between the context node and the original node.

[0045] (3) Initialize the context node set ...

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Abstract

The invention provides a network representation learning method based on multi-order proximity similarity. Compared with the traditional network representation based on structure analysis which only considers the relation between the first-order proximity similarity and the second-order proximity similarity, the invention focuses on the modeling of high-order adjacent similarity between nodes, thecalculation methods of different kinds of indirect neighbor similarity are designed respectively, especially considering that the information will fade with the distance in the process of network propagation, therefore, the method of the invention can predict the different neighboring nodes of the current node, more accurately find the neighboring nodes with the largest degree of correlation withthe target node, so that the expression vector with richer semantics and higher reliability and authenticity can be obtained.

Description

(1) Technical field [0001] The invention relates to the field of complex network analysis, in particular to a method for network representation learning based on multi-order adjacent similarity. (2) Background technology [0002] In daily life, network data is ubiquitous. For example, tens of thousands of website pages on the Internet constitute a network of web page links, and Weibo and Twitter constitute the network between people in people's social interaction. JD.com and Tmall constitute a network for users to shop. Therefore, the existence of information network has become the most common carrier and form in our life, and the research on information network has important academic value and application value. [0003] Network representation learning, also known as network embedding or graph embedding, essentially uses a low-dimensional, dense vector to represent the nodes in the network. This vector can reflect the structure of the network and can be used for clustering...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06K9/62G06Q50/00
CPCG06N3/08G06Q50/01G06F18/22Y02D30/70
Inventor 姚文斌张丽娟丁元浩杨超樊悦芹
Owner BEIJING UNIV OF POSTS & TELECOMM
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