Collaboration Recommendation of Finance Research Based on Multi-feature Fusion
Yu Chuanming1, Gong Yutian1, Zhao Xiaoli1, An Lu2()
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2School of Information Management, Wuhan University, Wuhan 430072, China
[Objective] Research collaboration builds an important social network system. This paper proposes a new recommendation model for research collaboration in finance, aiming to promote the scientific collaboration and improve research productivity. [Methods] First, we established the scientific collaboration networks at individuals, institutions and regions levels. Then, we established a recommendation model based on network neighbors and paths. Finally, we conducted empirical study to examine the model at three levels. [Results] A total of 68 905 articles published from 2000 to 2014 on finance were analyzed to construct their research collaboration networks. The AUC values ??of the proposed model at individual, institutional and regional levels were 84.25%, 87.34%, and 91.84%, respectively, which were higher than those of the traditional algorithms. [Limitations] The training and testing sets were only classified by time. More segmentation methods were needed to optimize the new model. [Conclusions] This study helps researchers find collaboration opportunities, and provides new directions for studies on scientific collaboration networks.
Dai C, Chen L, Li B, et al. Link Prediction in Multi-relational Networks Based on Relational Similarity[J]. Information Sciences, 2017, 394-395: 198-216.
doi: 10.1016/j.ins.2017.02.003
[2]
Moradabadi B, Meybodi M R.Link Prediction Based on Temporal Similarity Metrics Using Continuous Action Set Learning Automata[J]. Physica A: Statistical Mechanics & Its Applications, 2016, 460: 361-373.
doi: 10.1016/j.physa.2016.03.102
[3]
Yu C, Zhao X, An L, et al.Similarity-based Link Prediction in Social Networks: A Path and Node Combined Approach[J]. Journal of Information Science. DOI: 10.1177/016555151666 4039.
doi: 10.1177/0165551516664039
(Zhang Bin, Ma Feicheng.A Review on Link Prediction of Scientific Knowledge Network[J]. Journal of Library Science in China, 2015, 41(3): 30-47.)
[5]
Lu L, Zhou T.Link Prediction in Complex Networks: A Survey[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150-1170.
doi: 10.1016/j.physa.2010.11.027
[6]
Papadimitriou A, Symeonidis P, Manolopoulos Y.Fast and Accurate Link Prediction in Social Networking Systems[J]. Journal of Systems and Software, 2012, 8(5): 2119-2132.
doi: 10.1016/j.jss.2012.04.019
[7]
Chen J, Geyer W, Dugan C, et al.Make New Friends, but Keep the Old: Recommending People on Social Networking Sites[C]////Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI). 2009: 201-210.
[8]
Adamic L,Adar E.How to Search a Social Network[J]. Social Networks, 2005, 27(3): 187-203.
doi: 10.1016/j.socnet.2005.01.007
[9]
Tan P N, Steinbach M, Kumar V.Introduction to Data Mining[M]. The 1st Edition. Boston: Addison Wesley, 2005: 65-84.
[10]
Costa L da F, Rodrigues F A, Travieso G, et al. Characterization of Complex Networks: A Survey of Measurements[J]. Advances in Physics, 2007, 56(1): 167-242.
doi: 10.1080/00018730601170527
[11]
Katz L.A New Status Index Derived from Scientometric Analysis[J]. Psychometrika, 1953, 18(1): 39-43.
doi: 10.1007/BF02289026
[12]
Papadimitriou A, Symeonidis P, Manolopoulos Y.Fast and Accurate Link Prediction in Social Networking Systems[J]. Journal of Systems and Software, 2012, 8(5): 2119-2132.
doi: 10.1016/j.jss.2012.04.019
[13]
Pan J, Yang H, Faloutsos C,et al.Automatic Multimedia Cross-modal Correlation Discovery[C]////Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Ming, Seattle, WA,USA. 2004: 653-658.
[14]
Yan E, Guns R.Predicting and Recommending Collaborations: An Author-, Institution-, and Coutry-level Analysis[J]. Journal of Informetrics, 2014(8): 295-309.
doi: 10.1016/j.joi.2014.01.008
[15]
张斌. 科研合作网络中的链路预测研究[D]. 武汉: 武汉大学, 2016.
[15]
(Zhang Bin.Research on Link Prediction of Scientific Collaboration Network [D]. Wuhan: Wuhan University, 2016.)
(Liu Ping, Zheng Kailun, Zou De’an.Research on Cooperative Recommendation Based on LDA Model[I]. Information Studies: Theory & Application, 2015, 38(9): 79-85.)
doi: 10.16353/j.cnki.1000-7490.2015.09.016
(Lv Weiming, Wang Xiaomei, Han Tao.Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm[J]. Data Analysis and Knowledge Discovery, 2017, 1(4): 38-45.)
[18]
Yue Y, Finley T, Radlinski F, et al.A Support Vector Method for Optimizing Average Precision[C]////Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, Netherlands. New York: ACM, 2006:271-278.
[19]
Zhou T, Lv L, Zhang Y C.Predicting Missing Links via Local Information[J]. European Physical Journal B - Condensed Matter and Complex Systems, 2009, 71(4): 623-630.
doi: 10.1140/EPJB/E2009-00335-8
[20]
Evans T S, Lambiotte K, Panzarasa P.Community Structure and Patterns of Scientific Collaboration in Business and Management[J].Scientometrics, 2011, 9(1): 381-396.
doi: 10.1007/s11192-011-0439-1
(Ba Zhichao, Li Gang, Zhu Shiwei.Empirical Study and Modeling of Scientific Cooperation Behavior Based on Knowledge Hypernetwork[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(6): 630-639.)
doi: 10.3772/j.issn.1000-0135.2016.006.007