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On Link Stability Detection for Online Social Networks

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11029))

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Abstract

Link stability detection has been an important and long-standing problem within the link prediction domain. However, it has often been overlooked as being trivial and has not been adequately dealt with in link prediction. In this paper, we present an innovative method: Multi-Variate Vector Autoregression (MVVA) analysis to determine link stability. Our method adopts link dynamics to establish stability confidence scores within a clique sized model structure observed over a period of 30 days. Our method also improves detection accuracy and representation of stable links through a user-friendly interactive interface. In addition, a good accuracy to performance trade-off in our method is achieved through the use of Random Walk Monte Carlo estimates. Experiments with Facebook datasets reveal that our method performs better than traditional univariate methods for stability identification in online social networks.

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References

  1. Ozcan, A., Oguducu, S.G.: Multivariate temporal link prediction in evolving social networks. In: International Conference on Information Systems 2015 (ICIS-2015), pp. 113–118 (2015)

    Google Scholar 

  2. Mengshoel, O.J., Desai, R., Chen, A., Tran, B.: Will we connect again? machine learning for link prediction in mobile social networks. In: Eleventh Workshop on Mining and Learning with Graphs. Chicargo, Illinois 2013, pp. 1–6 (2013)

    Google Scholar 

  3. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)

    Article  Google Scholar 

  4. Chakraborty, T., Srinivasan, S., Ganguly, N., Bhowmick, S., Mukherjee, A.: Constant communities in complex networks (2013). arXiv preprint arXiv:1302.5794

  5. Sohl-Dickstein, J., Mudigonda, M., DeWeese, M.R.: Hamiltonian Monte Carlo without detailed balance. In: Proceedings of the 31st International Conference on Machine Learning (JMLR), vol. 32 (2014)

    Google Scholar 

  6. Farasat, A., Nikolaev, A., Srihari, S.N., Blair, R.H.: Probabilistic graphical models in modern social network analysis. Soc. Netw. Anal. Min. 5(1), 62 (2015)

    Article  Google Scholar 

  7. Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42. ACM (2009)

    Google Scholar 

  8. Girolami, M., Calderhead, B., Chin, S.A.: Riemannian manifold Hamiltonian Monte Carlo. Arxiv preprint, 6 July 2009

    Google Scholar 

  9. Meyer, H., Simma, H., Sommer, R., Della Morte, M., Witzel, O., Wolff, U., Alpha Collaboration: Exploring the HMC trajectory-length dependence of autocorrelation times in lattice QCD. Comput. Phys. Commun. 176(2), 91–97 (2007)

    Google Scholar 

  10. Read, J., Martino, L., Luengo, D.: Efficient monte carlo methods for multi-dimensional learning with classifier chains. Pattern Recogn. 47(3), 1535–1546 (2014)

    Article  Google Scholar 

  11. Pakman, A., Paninski, L.: Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions. In: Advances in Neural Information Processing Systems, pp. 2490–2498 (2013)

    Google Scholar 

  12. Hoffman, M.D., Gelman, A.: The No-U-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. J. Mach. Learn. Res. 15, 1351–1381 (2014)

    MathSciNet  MATH  Google Scholar 

  13. Rodriguez, A.: Modeling the dynamics of social networks using Bayesian hierarchical blockmodels. Stat. Anal. Data Min. 5(3), 218–234 (2012)

    Article  MathSciNet  Google Scholar 

  14. Hunter, D.R., Krivitsky, P.N., Schweinberger, M.: Computational statistical methods for social network models. J. Comput. Graph. Stat. 21(4), 856–882 (2012)

    Article  MathSciNet  Google Scholar 

  15. Nightingale, G., Boogert, N.J., Laland, K.N., Hoppitt, W.: Quantifying diffusion in social networks: a Bayesian approach. In: Animal Social Networks, pp. 38–52. Oxford University Press, Oxford (2014)

    Google Scholar 

  16. Fan, Y, Shelton. C.R.: Learning continuous-time social network dynamics. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 18 Jun 2009, pp. 161–168. AUAI Press (2009)

    Google Scholar 

  17. Yang, T., Chi, Y., Zhu, S., Gong, Y., Jin, R.: Detecting communities and their evolutions in dynamic social networks - a Bayesian approach. Mach. Learn. 82(2), 157–189 (2011)

    Article  MathSciNet  Google Scholar 

  18. Mossel, E., Sly, A., Tamuz, O.: Asymptotic learning on bayesian social networks. Probab. Theor. Relat. Fields 158(1–2), 127–157 (2014)

    Article  MathSciNet  Google Scholar 

  19. Gale, D., Kariv, S.: Bayesian learning in social networks. Games Econ. Behav. 45(2), 329–346 (2003)

    Article  MathSciNet  Google Scholar 

  20. Needham, C.J., Bradford, J.R., Bulpitt, A.J., Westhead, D.R.: A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3(8), e129 (2007)

    Article  Google Scholar 

  21. Gardella, C., Marre, O., Mora, T.: A tractable method for describing complex couplings between neurons and population rate. In: eNeuro, 1 July 2016, vol. 3, no. 4 (2016). ENEURO-0160

    Google Scholar 

  22. Getoor, L., Diehl, C.P.: Link mining: a random graph models approach. J. Soc. Struct. 7(2), 3–12 (2005). 2002 Apr survey. ACM SIGKDD Explorations Newsletter

    Google Scholar 

  23. Nguyen, N.P., Alim, M.A., Dinh, T.N., Thai, M.T.: A method to detect communities with stability in social networks. Soc. Netw. Anal. Min. 4(1), 1–15 (2014)

    Article  Google Scholar 

  24. Liu, F., Liu, B., Sun, C., Liu, M., Wang, X.: Deep belief network-based approaches for link prediction in signed social networks. Entropy 17(4), 2140–2169 (2015). Multidisciplinary Digital Publishing Institute

    Article  Google Scholar 

  25. Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)

    Google Scholar 

  26. Zhou, X., Tao, X., Rahman, M.M., Zhang, J.: Coupling topic modelling in opinion mining for social media analysis. In: Proceedings of the International Conference on Web Intelligence, pp. 533–540. ACM (2017)

    Google Scholar 

  27. Tao, X., Zhou, X., Zhang, J., Yong, J.: Sentiment analysis for depression detection on social networks. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 807–810. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49586-6_59

    Chapter  Google Scholar 

  28. Zhang, J., Tan, L., Tao, X., Zheng, X., Luo, Y., Lin, J.C.-W.: SLIND: Identifying stable links in online social networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 813–816. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91458-9_54

    Chapter  Google Scholar 

  29. Zhang, J., Tao, X., Tan, L.: On relational learning and discovery: a survey. Int. J. Mach. Learn. Cybern. 2(2), 88–114 (2018)

    Google Scholar 

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Acknowledgment

This research was partially supported by Guangxi Key Laboratory of Trusted Software (No. kx201615), Shenzhen Technical Project (JCYJ20170307151733005 and KQJSCX20170726103424709), Capacity Building Project for Young University Staff in Guangxi Province, Department of Education of Guangxi Province (No. ky2016YB149).

Author information

Authors and Affiliations

  1. Faculty of Engineering and Sciences, The University of Southern Queensland, Toowoomba, Australia

    Ji Zhang, Xiaohui Tao & Leonard Tan

  2. Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin

  3. School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China

    Hongzhou Li

  4. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China

    Liang Chang

Authors
  1. Ji Zhang
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  2. Xiaohui Tao
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  3. Leonard Tan
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  4. Jerry Chun-Wei Lin
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  5. Hongzhou Li
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  6. Liang Chang
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Corresponding authors

Correspondence to Ji Zhang or Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  3. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Regensburg, Regensburg, Germany

    Günther Pernul

  5. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Zhang, J., Tao, X., Tan, L., Lin, J.CW., Li, H., Chang, L. (2018). On Link Stability Detection for Online Social Networks. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-98809-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98808-5

  • Online ISBN: 978-3-319-98809-2

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