Method for recommending author conference based on scientific cooperation heterogeneous network analysis

Abstract

The invention discloses an author conference recommendation method based on scientific cooperation heterogeneous network analysis, and the method comprises the following steps: obtaining scientific cooperation data, and constructing a heterogeneous scientific cooperation network; carrying out the neighbor node sampling on the heterogeneous scientific cooperation network; carrying out the neighbor information aggregation on the sampled neighbor nodes; carrying out training and learning on the heterogeneous scientific cooperation network to obtain heterogeneous network representation; calculating the similarity between the representation vector of the author node and the representation vector of the conference node; and according to the similarity, recommending a conference which is highly similar to the author to the author. The method provided by the invention has a better effect in recommending the corresponding conference for the author and predicting the author to publish the article on the corresponding conference in the scientific cooperation network.

Description

technical field [0001] The invention belongs to the field of social network science and technology, and relates to an author meeting recommendation method based on scientific cooperation heterogeneous network analysis. Background technique [0002] In the real world, connections between individuals are ubiquitous, and these complex connections can be described by different forms of networks (social networks, citation networks, protein molecular interaction networks, power networks, etc.). As a common data carrier, data in the form of the network exists in all aspects of society. Deep mining of meaningful information in the network has very high academic value and potential application value. Although the structures and forms of various networks are complex and diverse, their basic components are nodes and edges. Therefore, the analysis and research on complex networks has universal significance. Since the network contains a large number of nodes and edges, it takes a lot of...

AI Technical Summary

Problems solved by technology

However, in the current network representation research, there is still a key problem: how to aggregate the information of the neighbors of the target node together

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