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| Application of Network Representation Learning in the Prediction of Scholar Academic Cooperation |
| Lin Yuan, Wang Kaiqiao, Liu Haifeng, Xu Kan, Ding Kun, Sun Xiaoling |
| Dalian University of Technology, Dalian 116024 |
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Abstract In the context of Big Data, scientific cooperation has become an important means to improve the level of scientific research and output. A research focus in recent years includes accurately identifying the cooperation objects that are suitable for scholars, institutions, and fields among the vast numbers of these entities. This study constructs a co-occurrence network of author-author, institution-institution, author-institution, author-keyword, and institution-keyword through the recorded data of scientific literature in the field of science of science. The network representation method is used to learn the context information of authors, institutions, and keywords in a network, and the information entity is represented as a low-dimensional dense vector of the same space. Finally, the mining of the cooperation object is achieved based on the similarity calculation of representation vector. The network representation learning method can realize a variety of heterogeneous information fusion, quantitatively calculate the correlation strength between each information entity, capture the relationship between scholars-scholars, scholars-institutions, and scholars-keywords in the research network, accurately explore potential collaborators, and partner institutions and keywords for scholars.
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Received: 08 July 2019
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1 孙晓玲, 丁堃. 深度学习中的表示学习研究及其对知识计量的影响[J]. 情报理论与实践, 2018, 41(9): 118-122.
2 刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261.
3 林原, 刘海峰, 王海龙, 等. 基于表示学习的学者间潜在合作机会挖掘[J]. 情报杂志, 2019, 38(5): 65-70.
4 MikolovT, ChenK, CorradoG, et al. Efficient estimation of word representations in vector space[OL]. https://arxiv.org/abs/1301.3781.
5 MikolovT, SutskeverI, ChenK,et al. Distributed representations of words and phrases and their compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. New York: ACM Press, 2013, 2: 3111-3119.
6 PerozziB, Al-RfouR, SkienaS. DeepWalk: Online learning of social representations[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2014: 701-710.
7 GroverA, LeskovecJ. node2vec: Scalable feature learning for networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2016: 855-864.
8 涂存超, 杨成, 刘知远, 等. 网络表示学习综述[J]. 中国科学: 信息科学, 2017, 47(8): 980-996.
9 余传明, 林奥琛, 钟韵辞, 等. 基于网络表示学习的科研合作推荐研究[J]. 情报学报, 2019, 38(5): 500-511.
10 汪志兵, 韩文民, 孙竹梅, 等. 基于网络拓扑结构与节点属性特征融合的科研合作预测研究[J]. 情报理论与实践, 2019, 42(8): 116-120, 109.
11 张金柱, 于文倩, 刘菁婕, 等. 基于网络表示学习的科研合作预测研究[J]. 情报学报, 2018, 37(2): 132-139.
12 熊回香, 杨雪萍, 蒋武轩, 等. 基于学术能力及合作关系网络的学者推荐研究[J]. 情报科学, 2019, 37(5): 71-78.
13 刘萍, 郑凯伦, 邹德安. 基于LDA模型的科研合作推荐研究[J]. 情报理论与实践, 2015, 38(9): 79-85.
14 余传明, 龚雨田, 赵晓莉, 等. 基于多特征融合的金融领域科研合作推荐研究[J]. 数据分析与知识发现, 2017(8): 39-47.
15 刘海峰. 基于Web of Science的学科表示方法研究[D]. 大连: 大连理工大学, 2019. |
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