Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm
Lv Weimin1,2, Wang Xiaomei3(), Han Tao1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2University of Chinese Academy of Sciences, Beijing 100049, China 3Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
[Objective] This paper proposes a method to recommend scientific research collaborators based on link prediction and machine learning, which improves the precision of traditional method. [Methods] First, we used Link Prediction Algorithm index to build the feature input, and adopted the Extremely Randomized Trees Algorithm to train the classifier. Then, we obtained the optimal weight combination with the traversal algorithm to combine the classification results linearly. Finally, we received the best recommendation of collaborators. [Results] The improved ET method had better performance than the existing ones in recommending the collaboration cities. Besides, the proposed method was less affected by factors such as the network structure, and could be used with more applications. [Limitations] Scientific research collaboration is affected by the cooperation motivation, geographical, language and many other factors. The weighted author network did not examine authors from the same cities or with the same organizations. [Conclusions] The propsoed method could produce better recommendation results, which might help universities, institutions and individuals identify academic collabortors.
吕伟民, 王小梅, 韩涛. 结合链路预测和ET机器学习的科研合作推荐方法研究*[J]. 数据分析与知识发现, 2017, 1(4): 38-45.
Lv Weimin,Wang Xiaomei,Han Tao. Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm. Data Analysis and Knowledge Discovery, 2017, 1(4): 38-45.
(Zhang Bin, Ma Feicheng.A Review on Link Prediction of Scientific Knowledge Network[J]. Journal of Library Science in China, 2015, 41(3): 99-113.)
doi: 10.13530/j.cnki.jlis.150016
[2]
Newman M E J. Scientific Collaboration Networks. I. Network Construction and Fundamental Results[J]. Physical Review E, 2001, 64(1): 016131.
doi: 10.1103/PhysRevE.64.016131
pmid: 11461355
[3]
Newman M E J. Scientific Collaboration Networks. II. Shortest Paths, Weighted Networks, and Centrality[J]. Physical Review E, 2001, 64(1): 016132.
doi: 10.1109/AUTEST.2006.283755
[4]
Newman M E J. The Structure of Scientific Collaboration Networks[J]. Proceedings of the National Academy of Sciences, 2001, 98(2): 404-409.
[5]
Barabási A L, Jeong H, Néda Z, et al.Evolution of the Social Network of Scientific Collaborations[J]. Physica A: Statistical Mechanics and Its Applications, 2002, 311(3-4): 590-614.
doi: 10.1016/S0378-4371(02)00736-7
[6]
De Solla Price D J. Little Science, Big Science… and Beyond[M]. New York: Columbia University Press, 1986.
[7]
Zuckerman H A.Patterns of Name Ordering Among Authors of Scientific Papers: A Study of Social Symbolism and Its Ambiguity[J]. American Journal of Sociology, 1968, 74(3): 276-291.
doi: 10.1086/224641
[8]
Kretschmer H.Author Productivity and Geodesic Distance in Bibliographic Co-authorship Networks, and Visibility on the Web[J]. Scientometrics, 2004, 60(3): 409-420.
doi: 10.1023/B:SCIE.0000034383.86665.22
[9]
Lü 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
[10]
Zhu B, Xia Y. An Information-theoretic Model for Link Prediction in Complex Networks[J]. Scientific Reports, 2015, 5: Article No. 13707.
doi: 10.1038/srep13707
pmid: 4558573
[11]
Guns R, Rousseau R.Predicting and Recommending Potential Research Collaborations[C]//Proceedings of ISSI. 2013: 1409-1418.
[12]
Guns R, Rousseau R.Recommending Research Collaborations Using Link Prediction and Random Forest Classifiers[J]. Scientometrics, 2014, 101(2): 1461-1473.
doi: 10.1007/s11192-013-1228-9
[13]
Yan E, Guns R.Predicting and Recommending Collaborations: An Author-, Institution-, and Country-level Analysis[J]. Journal of Informetrics, 2014, 8(2): 295-309.
doi: 10.1016/j.joi.2014.01.008
(Zhang Bin, Li Yating.A Review of the Evolution Model of Scientific Knowledge Network[J]. Journal of Library Science in China, 2016, 42(5): 85-101.)
[15]
Getoor L, Diehl C P.Link Mining: A Survey[J]. ACM SIGKDD Explorations Newsletter, 2005, 7(2): 3-12.
[16]
Liben-Nowell D, Kleinberg J.The Link Prediction Problem for Social Networks[J]. Journal of the Association for Information Science and Technology, 2007, 58(7): 1019-1031.
doi: 10.1002/asi.20591
[17]
吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5): 651-661.
[17]
(Lv Linyuan.Link Prediction on Complex Networks[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 651-661.)
[18]
Guns R.Missing Links: Predicting Interactions Based on a Multi-relational Network Structure with Applications in Informetrics [A]. // Missing Links: Predicting Interactions Based on a Multi-relational Network Structure with Applications in Informetrics[M]. Universiteit Antwerpen (Belgium). 2012.
[19]
Tylenda T, Angelova R, Bedathur S.Towards Time-aware Link Prediction in Evolving Social Networks[C]//Proceedings of the 3rd Workshop on Social Network Mining and Analysis. ACM, 2009: 1-10.
[20]
Wang C, Satuluri V, Parthasarathy S.Local Probabilistic Models for Link Prediction[C]//Proceedings of the 7th IEEE International Conference on Data Mining. IEEE, 2007: 322-331.
[21]
Guns R.Generalizing Link Prediction: Collaboration at the University of Antwerp as a Case Study[J]. Proceedings of the American Society for Information Science and Technology, 2009, 46(1): 1-15.
doi: 10.1002/meet.2009.1450460225
[22]
Mitchell T M. Machine Learning.1997[J]. Burr Ridge, IL: McGraw Hill, 1997, 45(37): 870-877.
[23]
Backstrom L, Leskovec J.Supervised Random Walks: Predicting and Recommending Links in Social Networks[C]// Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 2011: 635-644.
[24]
Arora S K, Porter A L, Youtie J, et al.Capturing New Developments in an Emerging Technology: An Updated Search Strategy for Identifying Nanotechnology Research Outputs[J]. Scientometrics, 2013, 95(1): 351-370.
doi: 10.1007/s11192-012-0903-6
[25]
Guns R.Bipartite Networks for Link Prediction: Can They Improve Prediction Performance[C]//Proceedings of ISSI. 2011: 249-260.
[26]
Adamic L A, Adar E.Friends and Neighbors on the Web[J]. Social Networks, 2003, 25(3): 211-230.
doi: 10.1016/S0378-8733(03)00009-1
[27]
Katz L.A New Status Index Derived from Sociometric Analysis[J]. Psychometrika, 1953, 18(1): 39-43.
doi: 10.1007/BF02289026
Pedregosa F, Varoquaux G, Gramfort A, et al.Scikit-learn: Machine Learning in Python[J]. Journal of Machine Learning Research, 2013, 12(10): 2825-2830.
doi: 10.1524/auto.2011.0951
[30]
Hanley J A, McNeil B J. The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve[J]. Radiology, 1982, 143(1): 29-36.
[31]
Herlocker J L, Konstan J A, Terveen L G, et al.Evaluating Collaborative Filtering Recommender Systems[J]. ACM Transactions on Information Systems (TOIS), 2004, 22(1): 5-53.
[32]
Zhou T, Ren J, Medo M, et al.Bipartite Network Projection and Personal Recommendation[J]. Physical Review E, 2007, 76(2): 046115.
[33]
Breiman L, Friedman J, Stone C J, et al.Classification and Regression Trees[M]. CRC Press, 1984.
[34]
Schubert T, Sooryamoorthy R.Can the Centre-periphery Model Explain Patterns of International Scientific Collaboration Among Threshold and Industrialised Countries? The Case of South Africa and Germany[J]. Scientometrics, 2010, 83(1): 181-203.
doi: 10.1007/s11192-009-0074-2
[35]
Boshoff N.South-South Research Collaboration of Countries in the Southern African Development Community (SADC)[J]. Scientometrics, 2010, 84(2): 481-503.
doi: 10.1007/s11192-009-0120-0
[36]
Pavlov M, Ichise R.Finding Experts by Link Prediction in Co-authorship Networks[C]//Proceedings of the 2nd International Conference on Finding Experts on the Web with Semantics. 2007.