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Social network link prediction method adopting knowledge graph embedding and time convolution network

A knowledge map and social network technology, applied in the field of social network link prediction, to achieve the effect of improving accuracy

Active Publication Date: 2020-10-16
NANCHANG HANGKONG UNIVERSITY
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Problems solved by technology

However, the existing social network link prediction methods still have the problem of prediction accuracy.

Method used

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  • Social network link prediction method adopting knowledge graph embedding and time convolution network
  • Social network link prediction method adopting knowledge graph embedding and time convolution network
  • Social network link prediction method adopting knowledge graph embedding and time convolution network

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Embodiment Construction

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0030] Embodiments of the present invention provide a social network link prediction method using knowledge graph embedding and temporal convolutional network, which realizes spontaneous extraction of link features between nodes and link prediction by establishing a temporal convolutional network model, The method includes steps S1-S3.

[00...

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Abstract

The invention discloses a social network link prediction method adopting knowledge graph embedding and a time convolution network. The method comprises the following steps: S1, processing original social sample data, extracting phrases and tuples of independent variables related to the phrases, constructing structured event tuples, linking the structured event tuples to a knowledge graph, constructing sub-graphs from the knowledge graph, and extracting event embedding vectors; S2, expressing the network structure of the social network by using an adjacency matrix, and fusing an event embeddingvector and a network adjacency matrix in a vector form; and S3, establishing a link prediction model based on the improved time convolution network, taking a fusion vector of the event embedding vector and the network adjacency matrix as input of the prediction model, and obtaining an optimal model through iterative training so as to predict a social network link. According to the invention, theprediction precision of the social network link can be improved.

Description

technical field [0001] The invention relates to the technical field of network analysis, in particular to a social network link prediction method using knowledge map embedding and time convolution network. Background technique [0002] Social networks include not only the network structure among users, but also the text information shared by a large number of users, which has the characteristics of large-scale, dynamic changes, and mixed information. Link prediction is one of the research and development directions in the field of data mining. Its research and development goal is to predict whether there are missing links between nodes in the current network or whether new links will be generated in the future network. [0003] Link prediction is mainly to use the existing network structure to predict the potential relationship between nodes. For example, in the prediction of friend relationship, the result of link prediction is pushed to the corresponding user as a "friend ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/00G06F16/36G06K9/62G06N3/04
CPCG06Q10/04G06Q50/01G06F16/367G06N3/049G06F18/25G06F18/214
Inventor 宋修洋刘琳岚
Owner NANCHANG HANGKONG UNIVERSITY
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