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An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent

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Abstract

How can we discover and estimate major events in complex social networks? Even with ever enlarging networks and data scale? Event detection and evaluation in social networks provide an effective solution, which has become the critical basis for many real applications, such as crisis management and decision making. The existing event detection methods mainly focus on text analysis that is limited in social media content and graphic feature statistic that needs calculate vast variables. Can we find an efficient way for generalized social networks with limited topology information? In this paper, a novel hybrid quantum swarm intelligence indexing method (HQSII) from the perspective of link prediction is proposed for the first time, which includes an optimal weight algorithm (OWA) and a fluctuation detection algorithm (FDA). The innovations behind HQSII lie in three aspects: (1) The mixed index that can universally describe the social network evolutions is proposed firstly, which explores the cooperation of different independent similarity indexes. OWA is further proposed to determine the optimal mixed index that achieves higher link prediction accuracy and better network evolution description than other independent and mixed similarity indexes. (2) To better avoid the interferences of routine network evolution fluctuations, the otherness of micro node evolutions is considered into link prediction. FDA is further proposed to quantify the abnormal fluctuations caused by events. (3) Based on OWA and FDA, HQSII is proposed for all the generalized social networks, which detects events by discovering abnormal fluctuations and evaluates events by analyzing fluctuation trends. Extensive experiments on theoretical and real-world social networks show that HQSII can accurately detect events and quantitatively evaluate event impacts in social networks with single and multiple events.

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References

  1. Adamic L.A., Adar E.: Friends and neighbors on the Web[J]. Soc. Networks. 211–230 (2003)

  2. Akoglu L., Tong H., Koutra D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29, 626–688 (2015)

    Article  MathSciNet  Google Scholar 

  3. Barabasi A.A.R.: Emergence of scaling in random networks. Science. 286, 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  5. Cannistraci C.V., Alanis-Lobato G., Ravasi T.: From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep. 3(4), 1613–1613 (2013)

    Article  Google Scholar 

  6. Chen B., Chen L.: A link prediction algorithm based on ant colony optimization. Appl. Intell. 41(3), 694–708 (2014)

    Article  Google Scholar 

  7. Chen B., Chen L., Li B.A.: Fast algorithm for predicting links to nodes of interest. Inf. Sci. 329, 552–567 (2016)

    Article  Google Scholar 

  8. Cho Y., Honorati M.: Entrepreneurship programs in developing countries: a meta regression analysis. Gen. Inform. 110–130 (2014)

  9. Cui Y., Pei J., Tang G., Luk W.S., Jiang D., Hua M.: Finding email correspondents in online social networks. World Wide Web-internet & Web Inf. Syst. 16(2), 195–218 (2013)

    Article  Google Scholar 

  10. Dong X., Mavroeidis D., Calabrese F., Frossard P.: Multiscale event detection in social media. Data Min. Knowl. Disc. 29, 1374–1405 (2014)

    Article  MathSciNet  Google Scholar 

  11. Ekeberg M., Hartonen T., Aurell E.: Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences. J. Comput. Phys. 276, 341–356 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gesek, G.: Quantum information theory. World Wide Web-internet & Web Information Systems (2012)

  13. Hanley J.A., Mcneil B.J.: The meaning and use of the area under a receiver operating chracteristic (roc) curve. Radiology. 143, 29–36 (1982)

    Article  Google Scholar 

  14. Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)

    Article  Google Scholar 

  15. Hu Z., Bao Y., Xiong T.: Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl. Soft Comput. 25, 15–25 (2014)

    Article  Google Scholar 

  16. Hu, W.B., Peng, C., Liang, H.L., Du, B.: Event detection method based on link prediction for social network evolution. J. Softw. (2015)

  17. Iglesias F., Zseby T.: Analysis of network traffic features for anomaly detection. Mach. Learn. 101, 59–84 (2014)

    Article  MathSciNet  Google Scholar 

  18. Jaccard P.: Etude comparative de la distribution florale dans une portion des Alpes et du Jura[M]. Impr. Corbaz. 37, 547 (1901)

    Google Scholar 

  19. Jamali, M., and Abolhassani, H.: Different aspects of social network analysis. IEEE. 66–67 (2006)

  20. Kleinberg, Liben Nowell J.: The link-prediction problem for social networks. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2003)

    Google Scholar 

  21. Leicht E.A., Holme P., Newman M.E.J.: Vertex similarity in networks. Phys. Rev. E. 73, 026120 (2012)

    Article  Google Scholar 

  22. Li Y., Jiao L., Shang R., Stolkin R.: Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Inf. Sci. 294, 408–422 (2015)

    Article  MathSciNet  Google Scholar 

  23. Lin Y.R., Chi Y., Zhu S., Sundaram H., Tseng B.L.: Analyzing communities and their evolutions in dynamic social networks. ACM Trans. Knowl. Discov. Data. 3(2), 307–308 (2009)

    Article  Google Scholar 

  24. Liu H.K., Lü L.Y., Zhou T.: Uncovering the network evolution mechanism by link prediction. Sci. Sin Phys. Mech. Astron. 41, 816–823 (2011)

    Article  Google Scholar 

  25. Liu J., Teo K.L., Wang X., Wu C.: An exact penalty function-based differential search algorithm for constrained global optimization. Soft. Comput. 20, 1305 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Mcculloh I.A., Carley K.M.: Social network change detection. Carnegie Mellon University School of Computer Science Institute for Software Research (2010)

  28. Musiał K., Kazienko P.: Social networks on the internet. World Wide Web-internet & Web Inf. Syst. 16(1), 31–72 (2012)

    Article  Google Scholar 

  29. Papadimitriou P., Dasdan A., Garcia-Molina H.: Web graph similarity for anomaly detection. J. Internet Serv. Appl. 1, 19–30 (2010)

    Article  Google Scholar 

  30. Pobiedina N., Ichise R.: Citation count prediction as a link prediction problem. Appl. Intell. 44, 252 (2014)

    Article  Google Scholar 

  31. Priebe C.E., Conroy J.M., Marchette D.J., Park Y.: Scan statistics on enron graphs. Comput. Math. Organ. Theory. 11(3), 229–247 (2005)

    Article  MATH  Google Scholar 

  32. Qian-Ming Z., Linyuan L., Wen-Qiang W., Tao Z.: Potential theory for directed networks. PLoS One. 2013, (2013)

  33. Rapoport A., Rapoport A.: Spread of information through a population with socio-structural bias. Bull. Math. Biophys. 15, 523 (1953)

    Article  MathSciNet  Google Scholar 

  34. Ravasz E., Somera A.L., Mongru D.A.: Hierarchical organization of modularity in metabolic networks. Science. 297, 1551–1555 (2002)

    Article  Google Scholar 

  35. Salton, G., McGill, M.H.: Introduction to modern information retrieval. Computerlinguistik McGraw-Hill, Inc. (1998)

  36. Serrà J., Arcos J.L.: Particle swarm optimization for time series motif discovery. Knowl.-Based Syst. 92, 127–137 (2015)

    Article  Google Scholar 

  37. Shi Y., Liu H., Gao L., Zhang G.: Cellular particle swarm optimization. Inf. Sci. 181, 4460–4493 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  38. Sørensen T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol. Skr. 1–34, (1948)

  39. Stilo G., Velardi P.: Efficient temporal mining of micro-blog texts and its application to event discovery. Data Min. Knowl. Disc. 30, 372–402 (2015)

    Article  MathSciNet  Google Scholar 

  40. Unankard S., Li X., Sharaf M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web-internet & Web Inf. Syst. 18(5), 1–25 (2014)

    Google Scholar 

  41. Wan, X., Milios, E., Kalyaniwalla, N., and Janssen, J. (2009) Link-based event detection in email communication networks. Sac Proceedings of the Acm Symposium on Applied Computing

    Book  Google Scholar 

  42. Wang Y., Meyer J.W., Ashraf M., Shull G.E.: A new genetic algorithm for release-time aware divisible-load scheduling. Circ. Res. 93(8), 776–782 (2014)

    Article  Google Scholar 

  43. Washio T., Motoda H.: State of the art of graph-based data mining. Acm Sigkdd Explor. Newsl. Homepage. 15(1), 59–68 (2003)

    Article  Google Scholar 

  44. Watts D.J., Strogatz S.H.: Collective dynamics of ‘small-world’ networks. Nature. 393(6684), 440–442 (1998)

    Article  Google Scholar 

  45. Yan Y., Yang Y., Meng D., Liu G., Tong W., Hauptmann A.G.: Event oriented dictionary learning for complex event detection. IEEE Trans. Image Process. 1867–1878 (2015)

  46. Yu H., Kim S.K., Kim J.: Scalable and parallelizable processing of influence maximization for large-scale social networks? 2014 I.E. 30th international conference on data. Engineering. 266–277 (2014)

  47. Zhang Q.M., Xu X.K., Zhu Y.X., Zhou T.: Measuring multiple evolution mechanisms of complex networks. Eprint Arxiv. 2014, (2014)

  48. Zhou X., Chen L.: Event detection over twitter social media streams. VLDB J. 23(3), 381–400 (2014)

    Article  MathSciNet  Google Scholar 

  49. Zhou T., Ren J., Medo M.: Bipartite network projection and personal recommendation. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 70–80 (2007)

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Acknowledgments

This work is supported by the National Natural Science Foundation, China (No.61572369, 70901060 and 61471274), the Hubei Province Natural Science Foundation (No. 2014CFB193 and 2015CFB423), the State Key Lab of Software Engineering Open Foundation (No. SKLSE 2010-08-15), the Youth Plan Found of Wuhan City (No.2011-50431101). And the authors also gratefully acknowledge all reviewers, and their comments have improved the presentation.

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, 430072, China

    Wenbin Hu, Huan Wang, Zhenyu Qiu, Cong Nie, Liping Yan & Bo Du

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  1. Wenbin Hu
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  2. Huan Wang
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  3. Zhenyu Qiu
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  4. Cong Nie
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  5. Liping Yan
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  6. Bo Du
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Correspondence to Wenbin Hu or Bo Du.

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Hu, W., Wang, H., Qiu, Z. et al. An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent. World Wide Web 20, 775–795 (2017). https://doi.org/10.1007/s11280-016-0416-y

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  • DOI: https://doi.org/10.1007/s11280-016-0416-y

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