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Detecting chaotic behaviors in dynamic complex social networks using a feature diffusion-aware model. (English) Zbl 1395.91382

Summary: This paper contributes in detecting chaotic behaviors in dynamic complex social networks using a new feature diffusion-aware model from two perspectives of abnormal links as well as abnormal nodes. The proposed approach constructs a probabilistic model of dynamic complex social networks and subsequently, applies it to detect chaotic behaviors by measuring deviations from the model. The predictive model considers the main processes of features’ dynamics, evolution of nodes’ features, feature diffusion, and link generation processes in dynamic complex social networks. The feature diffusion process indicates the process in which each node former features influence the future features of its neighbors. The proposed approach is validated by experiments on two real dynamic complex social network datasets of Google+ and Twitter. The approach uses some Markov Chain Monte Carlo sampling methods like Metropolis-Hastings algorithm and Slice sampling strategy to extract the model parameters, given these real datasets. Experimental results indicate the improved performance characteristics of the proposed approach in comparison with baseline approaches in terms of the performance measures of accuracy, F\(_{1}\)-score, Matthews Correlation Coefficient, recall, precision, area under ROC curve, and log-likelihood.{
©2018 American Institute of Physics}

MSC:

91D30 Social networks; opinion dynamics
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[1] Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. S., Chaotic, informational and synchronous behaviour of multiplex networks, Sci. Rep., 6, 22617, (2016) · doi:10.1038/srep22617
[2] Andris, C., LBSN data and the social butterfly effect (vision paper, (2015)
[3] Hejazi Nia, M., Butterfly effect in social network: Analysis of Churn under network influence, SSRN, (2012) · doi:10.2139/ssrn.2864986
[4] Ding, J.; Jiao, L.; Wu, J.; Liu, F., Prediction of missing links based on community relevance and ruler inference, Knowl.-Based Syst., 98, 200-215, (2016) · doi:10.1016/j.knosys.2016.01.034
[5] Ding, J.; Jiao, L.; Wu, J.; Hou, Y.; Qi, Y., Prediction of missing links based on multi-resolution community division, Physica A, 417, 76-85, (2015) · doi:10.1016/j.physa.2014.09.005
[6] Jin, M.; Girvan, M.; Newman, M. E. J., The structure of growing social networks, Phys. Rev. E, 64, 046132, (2001) · doi:10.1103/PhysRevE.64.046132
[7] Yasami, Y.; Safaei, F., A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks, Physica A, 492, 2166-2197, (2018) · Zbl 1515.91129 · doi:10.1016/j.physa.2017.11.134
[8] Cui, W.; Pua, C.; Xu, Z.; Cai, S.; Yang, J.; Michaelson, A., Bounded link prediction in very large networks, Physica A, 457, 202-214, (2016) · doi:10.1016/j.physa.2016.03.041
[9] Wang; Wu, Y.; Li, Q.; Jin, F.; Xiong, W., Link prediction based on hyperbolic mapping with community structure for complex networks, Physica A, 450, 609-623, (2016) · doi:10.1016/j.physa.2016.01.010
[10] Ahna, M.; Junga, W., Accuracy test for link prediction in terms of similarity index: The case of WS and BA models, Physica A, 429, 177-183, (2015) · doi:10.1016/j.physa.2015.01.083
[11] Sherkat, E.; Rahgozar, M.; Asadpour, M., Structural link prediction based on ant colony approach in social networks, Physica A, 419, 80-94, (2015) · doi:10.1016/j.physa.2014.10.011
[12] Ranshous, St.; Shen, S.; Koutra, D.; Faloutsos, C.; Samatova, N. F., Anomaly detection in dynamic networks: A survey, Wiley Interdisc. Rev.: Comput. Stat., 7, 3, 223-247, (2015) · doi:10.1002/wics.1347
[13] Chakrabarti, D., Autopart: Parameter-free graph partitioning and outlier detection, 112-124, (2004)
[14] Fanaee-T, H.; Gamab, J., Tensor-based anomaly detection: An interdisciplinary survey, J. Knowl.-Based Syst., 98, 130-147, (2016) · doi:10.1016/j.knosys.2016.01.027
[15] Chen, Y.; Miao, D.; Zhang, H., Neighborhood outlier detection, J. Expert Syst. Appl., 37, 12, 8745-8749, (2010) · doi:10.1016/j.eswa.2010.06.040
[16] Yang, J.; Leskovec, J., Community-affiliation graph model for overlapping community detection, (2012)
[17] Foulds, J.; Asuncion, A. U.; DuBois, C.; Butts, C. T.; Smyth, P., A dynamic relational infinite feature model for longitudinal social networks, (2011)
[18] Guo, F.; Hanneke, S.; Fu, W.; Xing, E. P., Recovering temporally rewiring networks: A model-based approach, (2007)
[19] Heaukulani, C.; Ghahramani, Z., Dynamic probabilistic models for latent feature propagation in social networks, (2013)
[20] Kairam, S.; Wang, D.; Leskovec, J., The life and death of online groups: Predicting group growth and longevity, (2012)
[21] Heard, N. A.; Weston, D. J.; Platanioti, K.; Hand, D. J., Bayesian anomaly detection methods for social networks, Ann. Appl. Stat. Inst. Math. Stat., 4, 2, 645-662, (2010) · Zbl 1194.62021
[22] Yasami, Y.; Safaei, F., A statistical infinite feature cascade-based approach to anomaly detection for dynamic social networks, Comput. Commun., 100, 52-64, (2017) · doi:10.1016/j.comcom.2016.11.010
[23] Griffiths, T.; Ghahramani, Z., The Indian buffet process: An introduction and review, J. Mach. Learn. Res., 12, 1185-1224, (2011) · Zbl 1280.62038
[24] Gershman, S. J.; Frazier, P. I.; Blei, D. M., Distance dependent infinite latent feature models, IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 37, 2, 334-345, (2015)
[25] Griffiths, T.; Ghahramani, Z., Infinite latent feature models and the Indian buffet process, Adv. Neural Inf. Process. Syst., 18, 475-482, (2005)
[26] Fu, W.; Song, L.; Xing, E. P., Dynamic mixed membership block model for evolving networks, (2009)
[27] Ho, Q.; Song, L.; Xing, E. P., Evolving cluster mixed-membership block model for time-varying networks, (2011)
[28] Ishiguro, K.; Iwata, T.; Ueda, N.; Tenenbaum, J., Dynamic infinite relational model for time-varying relational data analysis, (2010)
[29] Van Gael, J.; Teh, Y. W.; Ghahramani, Z., The infinite factorial hidden Markov model, (2009)
[30] Airoldi, E. M.; Blei, D. M.; Fienberg, S. E.; Xing, E. P., Mixed membership stochastic block models, J. Mach. Learn. Res. (JMLR), 9, 1981-2014, (2008) · Zbl 1225.68143
[31] Lü, L.; Zhou, T., Link prediction in complex networks: A survey, Physica A, 390, 1150-1170, (2011) · doi:10.1016/j.physa.2010.11.027
[32] Wang, P.; Xu, B. W., Link prediction in social networks: The state-of-the-art, Sci. China Inf. Sci., 58, 1, 1-58, (2014)
[33] Zhu, X. Y.; Lu, L.; Zhang, Q. M.; Zhou, T., Uncovering missing links with cold ends, Physica A, 391, 5769-5778, (2012) · doi:10.1016/j.physa.2012.06.003
[34] Papadimitriou, A.; Symeonidis, P.; Manolopoulos, Y., Fast and accurate link prediction in social networking systems, J. Syst. Software, 85, 2119-2132, (2012) · doi:10.1016/j.jss.2012.04.019
[35] Dong, E.; Li, J.; Xie, Z.; Wu, N., Bi-scale link prediction on networks, Chaos, Solitons Fractals, 78, 140-147, (2015) · Zbl 1353.90033 · doi:10.1016/j.chaos.2015.07.014
[36] Valverde-Rebaza, J.; Lopes, A., Exploiting behaviors of communities of twitter users for link prediction, Soc. Network Anal. Min., 3, 1063-1074, (2013) · doi:10.1007/s13278-013-0142-8
[37] Li, X.; Chen, H., Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach, Decis. Support Syst., 54, 880-890, (2013) · doi:10.1016/j.dss.2012.09.019
[38] Moradabadi, B.; Meybodi, M. R., Link prediction based on temporal similarity metrics using continuous action set learning automata, Physica A, 460, 361-373, (2016) · Zbl 1400.91482 · doi:10.1016/j.physa.2016.03.102
[39] Chen, Z.; Hendrix, W.; Samatova, N. F., Community-based anomaly detection in evolutionary networks, J. Intell. Inf. Syst. (JIIS), 39, 1, 59-85, (2012) · doi:10.1007/s10844-011-0183-2
[40] Hassanzadeh, R.; Nayak, R.; Stebila, D., Analyzing the effectiveness of graph metrics for anomaly detection in online social networks, Lecture Notes Comput. Sci.: Web Inf. Syst. Eng., 7651, 624-630, (2012) · doi:10.1007/978-3-642-35063-4
[41] Hanneke, S.; Fu, W.; Xing, E. P., Discrete temporal models of social networks, Electron. J. Stat. (EJS), 4, 585-605, (2010) · Zbl 1329.91113 · doi:10.1214/09-EJS548
[42] Snijders, T. A. B.; van de Bunt, G. G., Introduction to stochastic actor-based models for network dynamics, Soc. Networks, 32, 1, 44-60, (2010) · doi:10.1016/j.socnet.2009.02.004
[43] Handcock, M. S.; Robins, G.; Snijders, T., Assessing degeneracy in statistical models of social networks, J. Am. Stat. Assoc., 76, 33-50, (2003)
[44] Snijders, T., Statistical methods for network dynamics, 281-296, (2006)
[45] Snijders, T., The statistical evaluation of social network dynamics, Sociol. Methodol., 31, 1, 361-395, (2001) · doi:10.1111/0081-1750.00099
[47] Hoff, P. D.; Raftery, A. E.; Handcock, M. S., Latent space approaches to social network analysis, J. Am. Stat. Assoc. (JASA), 97, 460, 1090-1098, (2002) · Zbl 1041.62098 · doi:10.1198/016214502388618906
[48] Westveld, A. H.; Hoff, P. D., A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict, Ann. Appl. Stat., 5, 2, 843-872, (2011) · Zbl 1454.62509 · doi:10.1214/10-AOAS403
[49] Xing, E. P.; Fu, W.; Song, L., A state-space mixed-membership block model for dynamic network tomography, Ann. Appl. Stat., 4, 2, 535-566, (2010) · Zbl 1194.62133 · doi:10.1214/09-AOAS311
[50] Sarkar, P.; Siddiqi, S. M.; Gordon, G. J., A latent space approach to dynamic embedding of co-occurrence data, (2007)
[51] Kim, M.; Leskovec, J., Nonparametric multi-group membership model for dynamic networks, (2013)
[52] Miller, K. T.; Grifths, T. L.; Jordan, M. I., Nonparametric latent feature models for link prediction, (2009)
[53] Kim, M.; Leskovec, J., Latent multi-group membership graph model, (2012)
[54] Mørup, M.; Schmidt, M. N.; Hansen, L. K., Infinite multiple membership relational modeling for complex networks, (2011)
[55] Kim, M.; Leskovec, J., Multiplicative attribute graph model of real-world networks, Internet Math., 8, 1-2, 113-160, (2012) · Zbl 1245.05119 · doi:10.1080/15427951.2012.625257
[56] Scott, S. L., Bayesian methods for hidden Markov models, J. Am. Stat. Assoc. (JASA), 97, 457, 337-351, (2002) · Zbl 1073.65503 · doi:10.1198/016214502753479464
[57] Chiband, S.; Greenberg, E., Understanding the Metropolis-Hastings algorithm, Am. Stat., 49, 4, 327-335, (1995)
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