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International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem

GRMSE 2016: Geo-Spatial Knowledge and Intelligence pp 131–141Cite as

Link Prediction Based on Precision Optimization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 698))

Abstract

In complex networks, link prediction involves detecting both unknown links and links that may appear in the future. Recently, various approaches have been proposed to detect potential or future links in temporal social networks. To evaluate the performance of link prediction methods, precision are usually used to measure the accuracy of the predicting results. This paper proposes an algorithm based on the precision optimization. In the method, precision is treated as the objective function, and link prediction is transformed as an optimization problem. A group of topological features are defined for each ordered pair of nodes. Using those features as the attributes of the node pairs, link prediction can be treated as a binary classification where class label of each node pair is determined by whether there exists a directed link between the node pair. Then the binary classification problem can be solved by precision optimization. Empirical results show that our algorithm can achieve higher quality results of prediction than other algorithms.

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References

  1. Lichtenwalter, R.N.: New precepts and method in link prediction. In: Proceedings of ACM KDD 2010, pp. 243–252 (2010)

    Google Scholar 

  2. Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2011)

    Article  Google Scholar 

  3. Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: Fast and accurate link prediction in social networking systems. J. Syst. Soft. 85(9), 2119–2132 (2012)

    Article  Google Scholar 

  4. Hossmann, T., Nomikos, G., Spyropoulos, T., et al.: Collection and analysis of multi-dimensional network data for opportunistic networking research. Comput. Commun. 35(13), 1613–1625 (2012)

    Article  Google Scholar 

  5. Jahanbakhsh, K., King, V., Shoja, G.C.: Predicting missing contacts in mobile social networks. Pervasive Mob. Comput. 8(5), 698–716 (2012)

    Article  Google Scholar 

  6. Sun, Y., Barber, R., Gupta, M., et al.: Co-author relationship prediction in heterogeneous bibliographic networks. In: 2011 International Conference on IEEE Advances in Social Networks Analysis and Mining (ASONAM), pp. 121–128 (2011)

    Google Scholar 

  7. Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54(2), 880–890 (2013)

    Article  Google Scholar 

  8. Huang, Z., Lin, D.K.J.: The time-series link prediction problem with applications in communication surveillance. INFORMS J. Comput. 21(2), 286–303 (2009)

    Article  Google Scholar 

  9. Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)

    Article  Google Scholar 

  10. Salton, G., McGill, M.J.: Introduction to modern information retrieval. Inf. Process. Manag. 19(6), 402–403 (1983). ISBN 0-07-054484-0

    MATH  Google Scholar 

  11. Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes ET du Jura. Impr. Corbaz (1901)

    Google Scholar 

  12. Sorenson, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. Kongelige Danske Videnskabernes Selskab 5(1–34), 4–7 (1948)

    Google Scholar 

  13. Ravasz, E., et al.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)

    Article  Google Scholar 

  14. Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks. Phys. Rev. E 73(2), 026120 (2006)

    Article  Google Scholar 

  15. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)

    Article  Google Scholar 

  17. Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)

    Article  MATH  Google Scholar 

  18. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MathSciNet  MATH  Google Scholar 

  19. Chebotarev, P., Shamis, E.V.: The matrix-forest theorem and measuring relations in small social groups. Autom. Remote Control 58, 1505 (1997)

    MATH  Google Scholar 

  20. Lü, L., Jin, C.-H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80, 046122 (2009)

    Article  Google Scholar 

  21. Liu, W., Lü, L.: Link prediction based on local random walk. Europhys. Lett. 89, 58007 (2010)

    Article  Google Scholar 

  22. Klein, D.J., Randic, M.: Resistance distance. J. Math. Chem. 12(1), 81–95 (1993)

    Article  MathSciNet  Google Scholar 

  23. Fouss, F., Pirotte, A., Renders, J.M., et al.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)

    Article  Google Scholar 

  24. Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)

    Article  Google Scholar 

  25. Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)

    Google Scholar 

  26. Lü, L., Jin, C.H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4), 046122 (2009)

    Article  Google Scholar 

  27. Liu, W., Lü, L.: Link prediction based on local random walk. EPL (Europhys. Lett.) 89(5), 58007 (2010)

    Article  Google Scholar 

  28. Rao, J., Wu, B., Dong, Y.X.: Parallel link prediction in complex network using MapReduce. Ruanjian Xuebao/J. Softw. 23(12), 3175–3186 (2012)

    Google Scholar 

  29. Dong, Y.X., Ke, Q., Wu, B.: Link prediction based on node similarity. Comput. Sci. 38(7), 162 (2011)

    Google Scholar 

  30. Ou, Q., Jin, Y.-D., Zhou, T., Wang, B.-H., Yin, B.-Q.: Power-law strength-degree correlation from resource-allocation dynamics on weighted networks. Phys. Rev. E 75, 021102 (2007)

    Article  Google Scholar 

  31. Bhawsar, Y., Thakur, G.S., Thakur, R.S.: Model for link prediction in social network by genetic algorithm approach. Data Min. Knowl. Eng. 7(5), 191–196 (2015)

    Google Scholar 

  32. Verkaria, K., Clack, C.: Biases introduced by adaptive recombination operations. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), vol. 1, pp. 670–677 (1999)

    Google Scholar 

  33. Poli, R.: Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genet. Program Evolvable Mach. 2(2), 123–163 (2001)

    Article  MATH  Google Scholar 

  34. Rothlauf, F., Goldberg, David, E.: Pruefer numbers and genetic algorithms: a lesson on how the low locality of an encoding can harm the performance of gas. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 395–404. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_39

    Chapter  Google Scholar 

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Acknowledgements

This research was supported in part by the Chinese National Natural Science Foundation under grant Nos. 61379066, 61070047, 61379064, 61472344, 61402395, Natural Science Foundation of Jiangsu Province under contracts BK20130452, BK2012672, BK2012128, BK20140492 and Natural Science Foundation of Education Department of Jiangsu Province under contract 12KJB520019, 13KJB520026, 09KJB20013. Six talent peaks project in Jiangsu Province (Grant No. 2011-DZXX-032). Foundation of Graduate Student Creative Scientific Research of Jiangsu Province under contract CXZZ13_0172. China Scholarship Council also supported this work.

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Authors and Affiliations

  1. Department of Computer Science, Yangzhou University, Yangzhou, China

    Shensheng Gu & Ling Chen

Authors
  1. Shensheng Gu
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  2. Ling Chen
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Correspondence to Shensheng Gu .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Wuhan University, Wuhan, China

    Fuling Bian

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Gu, S., Chen, L. (2017). Link Prediction Based on Precision Optimization. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_14

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  • DOI: https://doi.org/10.1007/978-981-10-3966-9_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3965-2

  • Online ISBN: 978-981-10-3966-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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