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| Identification of Potential Research Partners Based on Two-Mode Network Analysis |
| Huang Lu1, Ni Xingxing1, Cheng Kefei2, Jia Xiang3 |
1.School of Management and Economics, Beijing Institute of Technology, Beijing 100081
2.China Northern Industry Co., Ltd., Beijing 100053
3.China Eastern Airlines Jiangsu Limited, Nanjing 210000 |
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Abstract With the increasing complexity and interdisciplinary nature of scientific research, several high-quality research results are found to be obtained owing to the collaboration between researchers. Based on the Web of Science databases, a novel method for identifying and evaluating potential scientific research partners via two-mode network link prediction is developed. The structure of the research content and the cooperative network of the research articles are comprehensively considered. Further, the dynamic nature of research interests and directions are reflected to help researchers rapidly identify potential partners using existing literature. As a part of the empirical research, this study consider scholars in the field of library and information science as an example to recommend partners.
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Received: 22 November 2019
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1 Yan E J, Ding Y. Discovering author impact: A PageRank perspective[J]. Information Processing & Management, 2011, 47(1): 125-134.
2 Yan E J, Guns R. Predicting and recommending collaborations: An author-, institution-, and country-level analysis[J]. Journal of Informetrics, 2014, 8(2): 295-309.
3 Weng J S, Lim E P, Jiang J, et al. TwitterRank: Finding topic-sensitive influential twitterers[C]// Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2010: 261-270.
4 Boyack K W, Klavans R. Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?[J]. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2389-2404.
5 Zhao D Z, Strotmann A. Evolution of research activities and intellectual influences in information science 1996-2005: Introducing author bibliographic-coupling analysis[J]. Journal of the American Society for Information Science and Technology, 2008, 59(13): 2070-2086.
6 Lu K, Wolfram D. Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches[J]. Journal of the American Society for Information Science and Technology, 2012, 63(10): 1973-1986.
7 Yang S L, Han R Z, Wolfram D, et al. Visualizing the intellectual structure of information science (2006-2015): introducing author keyword coupling analysis[J]. Journal of Informetrics, 2016, 10(1): 132-150.
8 Lee D H, Brusilovsky P, Schleyer T. Recommending collaborators using social features and MeSH terms[J]. Proceedings of the American Society for Information Science and Technology, 2011, 48(1): 1-10.
9 蒲姗姗. 基于知识互补的科研合作专家推荐模型研究[J]. 情报理论与实践, 2018, 41(8): 96-101.
10 郭改改, 钱宇华, 张晓琴, 等. 自主确定社区个数的二模网络社区发现算法[J]. 模式识别与人工智能, 2015, 28(11): 969-975.
11 薛娟, 丁长青, 原静. 基于二模复杂网络的WIKI知识网络研究——以WIKI中文百科学科词条为例[J]. 情报科学, 2016, 34(7): 136-140.
12 何劲, 关鹏, 王曰芬. 作者-主题关联的学科知识网络构建与演化分析[J]. 情报科学, 2019, 37(1):56-62, 67.
13 杨立英. 化学领域国际主要科研机构论文“共现”现象研究[J]. 科学观察, 2006(5): 10-17.
14 Zhang Q, Mao R, Li R. Spatial-temporal restricted supervised learning for collaboration recommendation[J]. Scientometrics, 2019, 119(3): 1497-1517.
15 张斌, 马费成. 科学知识网络中的链路预测研究述评[J]. 中国图书馆学报, 2015, 41(3): 99-113.
16 Guns R, Rousseau R. Recommending research collaborations using link prediction and random forest classifiers[J]. Scientometrics, 2014, 101(2): 1461-1473.
17 Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks[J]. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019-1031.
18 Benchettara N, Kanawati R, Rouveirol C. A supervised machine learning link prediction approach for academic collaboration recommendation[C]// Proceedings of the Fourth ACM Conference on Recommender Systems. New York: ACM Press, 2010: 253-256.
19 Zhang Y, Zhang G Q, Zhu D H, et al. Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics[J]. Journal of the Association for Information Science and Technology, 2017, 68(8): 1925-1939.
20 李纲, 徐健, 毛进, 等. 合著作者研究兴趣相似性分布研究[J]. 图书情报工作, 2017, 61(6): 92-98.
21 赵君, 廖建桥. 科研合作研究综述[J]. 科学管理研究, 2013, 31(2): 117-120.
22 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.
23 牛欣, 陈向东. 城市创新跨边界合作与辐射距离探析——基于城市间合作申请专利数据的研究[J]. 地理科学, 2013, 33(6): 659-667.
24 朱丽波. 科学合作网络的中心性分析[J]. 图书馆学研究, 2015(3): 97-100, 封三.
25 刘承良, 桂钦昌, 段德忠, 等. 全球科研论文合作网络的结构异质性及其邻近性机理[J]. 地理学报, 2017, 72(4): 737-752.
26 Shani G, Meek C, Gunawardana A. Hierarchical probabilistic segmentation of discrete events[C]// Proceedings of the Ninth IEEE International Conference on Data Mining. IEEE, 2009: 974-979.
27 李仲, 韩红旗, 吴广印, 等. 基于文本稀疏分布式表征的学术合作推荐[J]. 情报科学, 2019, 37(6): 113-118.
28 邱均平. 科学文献普赖斯指数的计算与分析[J]. 情报业务研究, 1989, 6(3): 170-172, 169.
29 Salovaara A, Helsinki U O, Lyytinen K, et al. High reliability in digital organizing: Mindlessness, the frame problem, and digital operations[J]. MIS Quarterly, 2019, 43(2): 555-578.
30 Luftman J, Lyytinen K, Zvi T B. Enhancing the measurement of information technology (IT) business alignment and its influence on company performance[J]. Journal of Information Technology, 2017, 32(1): 26-46. |
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