Research on Project Cooperation Prediction based on Metapath2vec
Authors: 
Xiaojun Zhou, Yulin YangAuthors Info & Claims
Xiaojun Zhou, Yulin YangAuthors Info & ClaimsPages 62 - 67
Abstract
Scientific research cooperation is an important way to carry out scientific projects and solve research problems, cooperative relationship prediction is one of the research hotspots at home and abroad in recent years. This paper focus on the academia of science, using Neo4j to build the network diagram of researchers and projects. Optimize on the basis of metapath2vec algorithm to predict cooperative relationship between researchers in different fields. Firstly, this paper studies and compares the cooperative prediction algorithms. Secondly, combined with the data set of project module of dataset, this paper optimizes and experiments the metapath2vec algorithm. Meanwhile, we design the weight coefficient under the influence of time factor, member contribution degree and project level, using heterogeneous knowledge graph to improve the computational efficiency and enhance the interpretability of prediction at the same time. Finally, the experiment and verification are carried out by using the project data of the National Fund Committee. The experimental result shows that the optimized cooperative prediction algorithm can take into account many factors such as time, member contribution, project level and so on, which helps predicting the potential cooperative relationship quickly, and the accuracy is improved by about 7% compared with the baseline.
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Published In
November 2021
110 pages
ISBN:9781450385220
DOI:10.1145/3503928
Copyright © 2021 Owner/Author.
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Association for Computing Machinery
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Published: 08 March 2022
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ICISE 2021
ICISE 2021: 2021 the 6th International Conference on Information Systems Engineering
November 12 - 14, 2021
Shanghai, China
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