Dynamics of information diffusion and its applications on complex networks

Physics Reports

Volumn 651, Issue , 2016, Pages 1-34

  Zhang, Zi Ke ^a,b   Liu, Chuang ^a   Zhan, Xiu Xiu ^a,c   Lu, Xin ^d,e   Zhang, Chu Xu ^f   Zhang, Yi Cheng ^a,g  

^a HANGZHOU NORMAL UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c Delft University of Technology ^*  (Netherlands)

^d NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^e KAROLINSKA INSTITUTE   (Sweden)

^f RUTGERS UNIVERSITY   (United States)

^g UNIVERSITY OF FRIBOURG   (Switzerland)

Author keywords

Complex networks; Information diffusion; Network dynamics

Indexed keywords

EID: 84994121036     PISSN: 03701573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physrep.2016.07.002     Document Type: Review

References (299)

1. [1] Kovanen, L., Kaski, K., Kertész, J., Saramäki, J., Temporal motifs reveal homophily, gender specific patterns, and group talk in call sequences. Proc. Natl. Acad. Sci. USA 110:45 (2013), 18070–18075.
2. [2] Iribarren, J.L., Moro, E., Impact of human activity patterns on the dynamics of information diffusion. Phys. Rev. Lett., 103, 2009, 038702.
3. [3] Karsai, M., Kivelä, M., Pan, R.K., Kaski, K., Kertész, J., Barabási, A.-L., Saramäki, J., Small but slow world: how network topology and burstiness slow down spreading. Phys. Rev. E, 83, 2011, 025102(R).
4. [4] Myers, S.A., Zhu, C.-G., Leskovec, J., Information diffusion and external influence in networks. Proceedings of the 18th ACM SIGKDD International Conference on Konwledge Discovery and Data Mining, 2012, ACM Press, New York, 33–41.
5. [5] Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J., Who says what to whom on twitter. Proceedings of the 20th ACM International Conference on World Wide Web, 2011, ACM Press, New York, 705–714.
6. [6] Goel, S., Watts, D.J., Goldstein, D.G., The structure of online diffusion networks. Proceedings of the 13rd ACM International Conference on Electronic Commerce, 2012, ACM Press, New York, 623–638.
7. [7] Crane, R., Sornette, D., Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl. Acad. Sci. USA 105:41 (2008), 15649–15653.
8. [8] Hodas, N., Lerman, K., The simple rules of social contagion. Sci. Rep., 4, 2015, 4343.
9. [9] Watts, D.J., A simple model of global cascades on random networks. Proc. Natl. Acad. Sci. USA 99:9 (2002), 5766–5771.
10. [10] Krapivsky, P.L., Redner, S., Volovik, D., Reinforcement-driven spread of innovations and fads. J. Stat. Mech., 2011, P12003.
11. [11] Granovetter, M., Threshold models of collective behavior. Am. J. Sociol. 83:6 (1978), 1420–1443.
12. [12] Nematzadeh, A., Ferrara, E., Flammini, A., Ahn, Y.-Y., Optimal network modularity for information diffusion. Phys. Rev. Lett., 113(8), 2014, 088701.
13. [13] Trpevski, D., Tang, W.K., Kocarev, L., Model for rumor spreading over networks. Phys. Rev. E, 81, 2010, 056102.
14. [14] Gross, T., Sayama, H., Adaptive Networks. 2009, Springer, Berlin, Heidelberg.
15. [15] Gross, T., Blasius, B., Adaptive coevolutionary networks: a review. J. R. Soc. Interface 5:20 (2008), 259–271.
16. [16] Nardini, C., Kozma, B., Barrat, A., Who's talking first? Consensus or lack thereof in coevolving opinion formation models. Phys. Rev. Lett., 100(15), 2008, 158701.
17. [17] Shen, Z., Wang, W.-X., Fan, Y., Di, Z., Lai, Y.-C., Reconstructing propagation networks with natural diversity and identifying hidden sources. Nature Commun., 5, 2014, 4323.
18. [18] Pinto, P.C., Thiran, P., Vetterli, M., Locating the source of diffusion in large-scale networks. Phys. Rev. Lett., 109(6), 2012, 068702.
19. [19] Shen, Z., Cao, S., Wang, W.-X., Di, Z., Stanley, H.E., Locating the source of diffusion in complex networks by time-reversal backward spreading. Phys. Rev. E, 93(3), 2016, 032301.
20. [20] Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., Faloutsos, C., Epidemic thresholds in real networks. ACM Trans. Inf. Syst. Secur., 10(4), 2008, 1.
21. [21] Tong, H., Prakash, B.A., Tsourakakis, C., Eliassi-Rad, T., Faloutsos, C., Chau, D.H., On the vulnerability of large graphs. Proceedings of the 10th International Conference on Data Mining, 2010, IEEE Press, 1091–1096.
22. [22] Prakash, B.A., Adamic, L., Iwashyna, T.J., Tong, H.H., Christos, F., Fractional immunization in networks. Proceedings of the 2013 SIAM International Conference on Data Mining, 2013, SIAM Press, 659–667.
23. [23] Tong, H., Prakash, B.A., Eliassi-Rad, T., Faloutsos, M., Faloutsos, C., Gelling, and melting, large graphs by edge manipulation. Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012, ACM Press, New York, 245–254.
24. [24] Le, L.T., Eliassi-Rad, T., Tong, H., Met: A fast algorithm for minimizing propagation in large graphs with small eigen-gaps. Proceedings of the 2015 SIAM International Conference on Data Mining, 2015, SIAM Press, 694–702.
25. [25] Zhang, C., Eliassi-Rad, T., Minimizing dissemination in a population while maintaining its community structure. The SIGKDD Workshop on Population Informatics for Big Data, 2015, ACM Press, Sydney.
26. [26] McAfee, A.P., Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan Manage. Rev. 47:3 (2006), 21–28.
27. [27] Page, M., Molina, M., Jones, G., Makarov, D., The Mobile Economy. 2016, AT Kearny.
28. [28] Zhao, H., Dong, Z., Li, T., Wang, X., Pang, C., Segmenting time series with connected lines under maximum error bound. Inform. Sci. 345 (2016), 1–8.
29. [29] Zhu, Q., Lin, Q., Du, Z., Liang, Z., Wang, W., Zhu, Z., Chen, J., Huang, P., Ming, Z., A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm. Inform. Sci. 345 (2016), 177–198.
30. [30] Sorensen, J.H., Hazard warning systems: Review of 20~years of progress. Nat. Hazards. Rev. 1:2 (2000), 119–125.
31. [31] Bellman, S., Potter, R.F., Treleaven-Hassard, S., Robinson, J.A., Varan, D., The effectiveness of branded mobile phone apps. J. Interact. Mark. 25:4 (2011), 191–200.
32. [32] Katz, J.E., Aakhus, M., Perpetual Contact: Mobile Communication, Private Talk, Public Performance. 2002, Cambridge University Press.
33. [33] Schiller, J., Voisard, A., Location-based Services. 2004, Morgan Kaufmann Publishers.
34. [34] Dhar, S., Varshney, U., Challenges and business models for mobile location-based services and advertising. Commun. ACM 54:5 (2011), 121–128.
35. [35] Park, M.-H., Hong, J.-H., Cho, S.-B., Location-based recommendation system using bayesian user's preference model in mobile devices. Proceedings of the 4th International Conference on Ubiquitous Intelligence and Computing, 2007, Springer, 1130–1139.
36. [36] Küpper, A., Location-based Services: Fundamentals and Operation. 2005, John Wiley & Sons.
37. [37] A. Chatfield, U. Brajawidagda, Twitter tsunami early warning network: a social network analysis of twitter information flows, in: Proceedings of the 23rd Australasian Conference on Information Systems, 2012, pp. 1–10.
38. [38] Lin, Q., Liu, Z., Yan, Q., Du, Z., Coello, C.A.C., Liang, Z., Wang, W., Chen, J., Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inform. Sci. 339 (2016), 332–352.
39. [39] DiMicco, J., Millen, D.R., Geyer, W., Dugan, C., Brownholtz, B., Muller, M., Motivations for social networking at work. Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, 2008, ACM Press, New York, 711–720.
40. [40] Acquisti, A., Gross, R., Imagined communities: Awareness, information sharing, and privacy on the facebook. Proceedings of the 6th International Conference on Privacy Enhancing Technologies, 2006, Springer, 36–58.
41. [41] Kwak, H., Lee, C., Park, H., Moon, S., What is twitter, a social network or a news media?. Proceedings of the 19th International Conference on World Wide Web, 2010, ACM Press, New York, 591–600.
42. [42] Brabham, D.C., Crowdsourcing. 2013, Mit Press.
43. [43] Howe, J., The rise of crowdsourcing. Wired Magazine 14:6 (2006), 1–4.
44. [44] Tang, J.C., Cebrian, M., Giacobe, N.A., Kim, H.-W., Kim, T., Wickert, D.B., Reflecting on the darpa red balloon challenge. Commun. ACM 54:4 (2011), 78–85.
45. [45] Meier, P., Crisis mapping in action: How open source software and global volunteer networks are changing the world, one map at a time. J. Map. Geogr. Libr. 8:2 (2012), 89–100.
46. [46] Belleflamme, P., Lambert, T., Schwienbacher, A., Crowdfunding: Tapping the right crowd. J. Bus. Venturing 29:5 (2014), 585–609.
47. [47] Leskovec, J., Adamic, L.A., Huberman, B.A., The dynamics of viral marketing. ACM. T. Web, 1(1), 2007, 5.
48. [48] Richardson, M., Domingos, P., Mining knowledge-sharing sites for viral marketing. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, ACM Press, New York, 61–70.
49. [49] Lai, Z., Wong, W.K., Xu, Y., Yang, J., Zhang, D., Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans. Neural Netw. Learn. Syst. 27:4 (2016), 723–735.
50. [50] Jurvetson, S., What exactly is viral marketing. Red Herring 78 (2000), 110–112.
51. [51] Watts, D.J., Peretti, J., Frumin, M., Viral Marketing for the Real World. 2007, Harvard Business School Pub.
52. [52] Vaidya, M., Ice bucket challenge cash may help derisk als drug research. Nat. Methods, 20(10), 2014, 1080.
53. [53] Kaplan, A.M., Haenlein, M., Two hearts in three-quarter time: How to waltz the social media/viral marketing dance. Bus. Horiz. 54:3 (2011), 253–263.
54. [54] Fournier, S., Avery, J., The uninvited brand. Bus. Horiz. 54:3 (2011), 193–207.
55. [55] Watts, D.J., Dodds, P.S., Influentials, networks, and public opinion formation. J. Consum. Res. 34:4 (2007), 441–458.
56. [56] A. Tumasjan, T.O. Sprenger, P.G. Sandner, I.M. Welpe, Predicting elections with twitter: What 140 characters reveal about political sentiment, in: Proceedings of the Fourth International AAAI Conference on Web and Social Media, 2010, pp. 178–185.
57. [57] Graham, T., Broersma, M., Hazelhoff, K., van't Haar, G., Between broadcasting political messages and interacting with voters: The use of twitter during the 2010 UK general election campaign. Inform. Commun. Soc. 16:5 (2013), 692–716.
58. [58] Tetlock, P.C., Giving content to investor sentiment: The role of media in the stock market. J. Financ. 62:3 (2007), 1139–1168.
59. [59] Mao, R., Zhang, P., Li, X., Liu, X., Lu, M., Pivot selection for metric-space indexing. Int. J. Mach. Learn.Cybern. 7:2 (2016), 311–323.
60. [60] Duda, J., Korus, P., Gadgil, N.J., Tahboub, K., Delp, E.J., Image-like 2d barcodes using generalizations of the Kuznetsov–Tsybakov problem. IEEE Trans. Inf. Forensics Secur. 11:4 (2016), 691–703.
61. [61] Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A., Twitter power: Tweets as electronic word of mouth. J. Am. Soc. Inf. Sci. Tec. 60:11 (2009), 2169–2188.
62. [62] B. O'Connor, R. Balasubramanyan, B.R. Routledge, N.A. Smith, From tweets to polls: Linking text sentiment to public opinion time series, in: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, 2010, pp. 122–129.
63. [63] Burt, R.S., Social contagion and innovation: Cohesion versus structural equivalence. Am. J. Sociol. 92:6 (1987), 1287–1335.
64. [64] Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J., Structural diversity in social contagion. Proc. Natl. Acad. Sci. USA 109:16 (2012), 5962–5966.
65. [65] McPherson, M., Smith-Lovin, L., Cook, J.M., Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27 (2001), 415–444.
66. [66] Centola, D., The spread of behavior in an online social network experiment. Science 329 (2010), 1194–1197.
67. [67] Kreindler, G.E., Young, H.P., Rapid innovation diffusion in social networks. Proc. Natl. Acad. Sci. USA 111:Suppl. 3 (2014), 10881–10888.
68. [68] Coleman, J.S., Katz, E., Menzel, H., Medical innovation: A diffusion study. Adm. Sci. Quart. 12:2 (1967), 355–361.
69. [69] Rogers, E.M., Diffusion of Innovations. 2003, The Free Press, New York.
70. [70] Ciliberti, S., Martin, O.C., Wagner, A., Innovation and robustness in complex regulatory gene networks. Proc. Natl. Acad. Sci. USA 104:34 (2007), 13591–13596.
71. [71] Tang, W., Li, H., Luo, W., Huang, J., Adaptive steganalysis based on embedding probabilities of pixels. IEEE Trans. Inf. Forensics Secur. 11:4 (2016), 734–745.
72. [72] Wang, G., Zhang, S., Wu, K., Zhang, Q., Ni, L.M., Tim: Fine-grained rate adaptation in wlans. IEEE Trans. Mob. Comput. 15:3 (2016), 748–761.
73. [73] Wang, G., Guan, Y., Wang, Y., Shao, Z., Energy-aware assignment and scheduling for hybrid main memory in embedded systems. Computing 98:3 (2016), 279–301.
74. [74] Berger, N., Borgs, C., Chayes, J.T., Saberi, A., On the spread of viruses on the internet. Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms, 2005, Society for Industrial and Applied Mathematics, 301–310.
75. [75] Wang, P., González, M.C., Hidalgo, C.A., Barabási, A.-L., Understanding the spreading patterns of mobile phone viruses. Science 324:5930 (2009), 1071–1076.
76. [76] Serrano, E., Iglesias, C.A., Garijo, M., A survey of twitter rumor spreading simulations. Computational Collective Intelligence, 2015, Springer, 113–122.
77. [77] T. Holz, M. Steiner, F. Dahl, E. Biersack, F.C. Freiling, Measurements and mitigation of peer-to-peer-based botnets: A case study on storm worm, in: Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, no. 9, 2008.
78. [78] Doerr, B., Fouz, M., Friedrich, T., Why rumors spread so quickly in social networks. Commun. ACM 55:6 (2012), 70–75.
79. [79] O. Oh, K.H. Kwon, H.R. Rao, An exploration of social media in extreme events: Rumor theory and twitter during the haiti earthquake 2010, in: Proceedings of the 31st International Conference on Information Systems, 2010, pp. 1–13.
80. [80] Y. Tanaka, Y. Sakamoto, T. Matsuka, Transmission of rumor and criticism in twitter after the great japan earthquake, in: Annual Meeting of the Cognitive Science Society, 2012, p. 2387.
81. [81] Leskovec, J., Backstrom, L., Kleinberg, J., Meme-tracking and the dynamics of the news cycle. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, ACM Press, New York, 497–506.
82. [82] Matsubara, Y., Sakurai, Y., Prakash, B.A., Li, L., Faloutsos, C., Rise and fall patterns of information diffusion: Model and implications. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, ACM Press, New York, 6–14.
83. [83] Yang, J., Leskovec, J., Patterns of temporal variation in online media. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, 2011, ACM Press, New York, 177–186.
84. [84] Zhang, X., Xu, C., Sun, X., Baciu, G., Schatten-q regularizer constrained low rank subspace clustering model. Neurocomputing 182 (2016), 36–47.
85. [85] Ciampaglia, G.L., Flammini, A., Menczer, F., The production of information in the attention economy. Sci. Rep., 5, 2015, 9452.
86. [86] Sano, Y., Yamada, K., Watanabe, H., Takayasu, H., Takayasu, M., Empirical analysis of collective human behavior for extraordinary events in the blogosphere. Phys. Rev. E, 87, 2013, 012805.
87. [87] Lehmann, J., Goncalves, B., Ramasco, J.J., Cattuto, C., Dynamical classes of collective attention in twitter. Proceedings of the 21st ACM International Conference on World Wide Web, 2012, ACM Press, New York, 251–260.
88. [88] Zhang, P., Wang, X.-F., Li, B.-X., On predicting Twitter trend: factors and models. Proceedings of the 2013 ACM International Conference on Advances in Social Networks Analysis and Mining, 2013, ACM Press, New York, 1427–1429.
89. [89] Lin, J.C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Tseng, V.S., Efficient algorithms for mining high-utility itemsets in uncertain databases. Knowl.-Based Syst. 96 (2016), 171–187.
90. [90] Zhou, Q.F., Mow, W.H., Zhang, S., Toumpakaris, D., Two-way decode-and-forward for low-complexity wireless relaying: Selective forwarding versus one-bit soft forwarding. IEEE Trans. Wireless Commun. 15:3 (2016), 1866–1880.
91. [91] Wang, S.-Z., Yan, Z., Hu, X., Yu, P.S., Li, Z.-J., Burst time prediction in cascades. Proceedings of the 29th AAAI Conference on Aritificial Intelligence, 2015, AAAI Press, 325–331.
92. [92] Cheng, J.-T., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J., Can cascades be predicted?. Proceedings of the 23rd ACM International Conference on World Wide Web, 2014, ACM Press, New York, 925–936.
93. [93] Lü, L.-Y., Chen, D.-B., Zhou, T., The small world yields the most effective information spreading. New J. Phys., 13, 2011, 123005.
94. [94] Zhao, Q.-Y., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J., SEISMIC: a self-exciting point process model for perdicting tweet popularity. Proceedings of the 21st ACM International Conference on Knowledge Discovery and Data Mining, 2015, ACM Press, New York, 1513–1522.
95. [95] Romero, D.M., Meeder, B., Kleinberg, J., Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. Proceedings of the 20th ACM International Conference on World Wide Web, 2011, ACM Press, New York, 695–704.
96. [96] Vicario, M.D., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H.E., Quattrociocchi, W., The spreading of misinformation online. Proc. Natl. Acad. Sci. USA 113:3 (2016), 554–559.
97. [97] Coscia, M., Average is boring: how similarity kills a meme's success. Sci. Rep., 4, 2014, 6477.
98. [98] Funk, S., Gilad, E., Watkins, C., Jansen, V.A.A., The spread of awareness and its impact on epidemic outbreaks. Proc. Natl. Acad. Sci. USA 106:16 (2009), 6872–6877.
99. [99] Cui, L., Li, G., Lin, Q., Chen, J., Lu, N., Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput. Oper. Res. 67 (2016), 155–173.
100. [100] Pan, W., Xia, S., Liu, Z., Peng, X., Ming, Z., Mixed factorization for collaborative recommendation with heterogeneous explicit feedbacks. Inform. Sci. 332 (2016), 84–93.
101. [101] Ma, Z., Sun, A., Cong, G., Will this # hashtag be popular tomorrow?. Proceedings of the 35th ACM International Conference on Research and Development in Information Retrieval, 2012, ACM Press, New York, 1173–1174.
102. [102] Zhou, G., Yu, L., Zhang, C.-X., Liu, C., Zhang, Z.-K., Zhang, J.-L., A novel approach for generating personalized mention list on micro-blogging system. Proceedings of the 15th IEEE International Conference on Data Mining Workshops, 2015, IEEE Press, 1368–1374.
103. [103] Chen, K.-L., Chen, T.-Q., Zheng, G.-Q., Jin, O., Yao, E.-P., Yu, Y., Collaborative personalized tweet recommendation. Proceedings of the 35th ACM International Conference on Research and Development in Information Retrieval, 2012, ACM Press, New York, 661–670.
104. [104] Alis, C.M., Lim, M.T., Moat, H.S., Barchiesi, D., Preis, T., Bishop, S.R., Quantifying regional differences in the length of twitter messages. PLoS ONE, 10(4), 2015, e0122278.
105. [105] Kwon, S., Cha, M., Modeling bursty temporal pattern of rumors. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 2014, AAAI Press, 650–651.
106. [106] Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y., Prominent features of rumor propagation in online social media. Proceedings of the 13th International Conference on Data Mining, 2013, IEEE Press, 1103–1108.
107. [107] Ma, J., Gao, W., Wei, Z.-Y., Lu, Y.-M., Wong, K.-F., Detect rumors using time series of social context information on microblogging websites. Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015, ACM Press, New York, 1751–1754.
108. [108] Centola, D., An experimental study of homophily in the adoption of health behavior. Science 334 (2011), 1269–1272.
109. [109] Bakshy, E., Rosenn, I., Marlow, C., Adamic, L., The role of social networks in information diffusion. Proceedings of the 21st ACM International Conference on World Wide Web, 2012, ACM Press, New York, 519–528.
110. [110] Ardon, S., Bagchi, A., Mahanti, A., Ruhela, A., Seth, A., Tripathy, R.M., Triukose, S., Spatio-temporal and events based analysis of topic popularity in twitter. Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013, ACM Press, New York, 219–228.
111. [111] Lerman, K., Ghosh, R., Information contagion: an empirical study of the spread of news on digg and twitter social networks. Proceedings of the 4th AAAI International Conference on Weblogs and Social Media, 2010, AAAI Press, 90–97.
112. [112] Arnaboldi, V., Conti, M., Gala, M., Passarella, A., Pezzoni, F., Ego network structure in online social networks and its impact on information diffusion. Comput. Commun. 76 (2016), 26–41.
113. [113] Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.-Z., Social influence locality for modeling retweeting behaviors. Proceedings of the 23rd International Joint Conference on Artifical Intelligence, 2013, AAAI Press, 2761–2767.
114. [114] Granovetter, M.S., The strength of weak ties. Am. J. Sociol. 78 (1973), 1360–1380.
115. [115] Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L., Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. USA 104:18 (2007), 7332–7336.
116. [116] Bakshy, E., Messing, S., Adamic, L.A., Exposure to ideologically diverse news and opinion on facebook. Science 348 (2015), 1130–1132.
117. [117] Holme, P., Saramäki, J., Temporal networks. Phys. Rep. 519 (2012), 97–125.
118. [118] Myers, S., Leskovec, J., The bursty dynamics of the twitter information network. Proceedings of the 23rd ACM International Conference on World Wide Web, 2014, ACM Press, New York, 913–924.
119. [119] Pastor-Satorras, R., Castellano, C., Van~Mieghem, P., Vespignani, A., Epidemic processes in complex networks. Rev. Modern Phys. 87:3 (2015), 925–979.
120. [120] Goel, S., Anderson, A., Hofman, J., Watts, D.J., The structural virality of online diffusion. Manag. Sci. 62:1 (2016), 180–196.
121. [121] Leskovec, J., Adamic, L.A., Huberman, B.A., The dynamics of viral marketing. ACM T. WEB, 1(1), 2007, 5.
122. [122] Newman, M.E.J., Spread of epidemic disease on networks. Phys. Rev. E, 66, 2002, 016128.
123. [123] Goldenberg, J., Libai, B., Muller, E., Talk of the network: A complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12:3 (2001), 211–223.
124. [124] Bao, P., Shen, H.-W., Chen, W., Cheng, X.-Q., Cumulative effect in information diffusion: empirical study on a microblogging network. PLoS ONE, 8(10), 2013, e76027.
125. [125] Pei, S., Muchnik, L., Tang, S.-T., Zheng, Z.-M., Makse, H.A., Exploring the complex pattern of information spreading in online blog communities. PLoS ONE, 10, 2015, e0126894.
126. [126] Gómez, V., Kappen, H., Kaltenbrunner, A., Modeling the structure and evolution of discussion cascades. Proceedings of the 22nd ACM International Conference on Hypertext and Hypermedia, 2011, ACM Press, New York, 181–190.
127. [127] Wang, D.-S., Wen, Z., Tong, H.-H., Lin, C.-Y., Song, C.-M., Barabási, A.-L., Information spreading in context. Proceedings of the 20th ACM International Conference on World Wide Web, 2011, ACM Press, New York, 735–744.
128. [128] Bi, S., Zhang, R., Placement optimization of energy and information access points in wireless powered communication networks. IEEE Trans. Wirel. Commun. 15:3 (2016), 2351–2364.
129. [129] Guo, C., Liao, B., Huang, L., Lin, X., Zhang, J., On convexity of fairness-aware energy-efficient power allocation in spectrum-sharing networks. IEEE Commun. Lett. 20:3 (2016), 534–537.
130. [130] Guo, C., Liao, B., Huang, L., Li, Q., Lin, X., Convexity of fairness-aware resource allocation in wireless powered communication networks. IEEE Commun. Lett. 20:3 (2016), 474–477.
131. [131] Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J., Tiwari, M., Global diffusion via cascading invitations: structure, growth, and homophily. Proceedings of the 24th ACM International Conference on World Wide Web, 2015, ACM Press, New York, 66–76.
132. [132] Gomez-Rodriguez, M., Balduzzi, D., Schölkopf, B., Uncovering the temporal dynamics of diffusion networks. Proceedings of the 28th ACM International Conference on Machine Learning, 2011, ACM Press, New York, 561–568.
133. [133] Cohen, R., Erez, K., ben Avraham, D., Havlin, S., Breakdown of the internet under intentional attack. Phys. Rev. Lett. 86 (2001), 3682–3685.
134. [134] Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A., Identification of influential spreaders in complex networks. Nat. Phys. 6 (2010), 888–893.
135. [135] Pei, S., Muchnik, L., Andrade, J.S.J., Zheng, Z.-M., Makse, H.A., Searching for superspreaders of information in real-world social media. Sci. Rep., 4, 2014, 5547.
136. [136] Lü, L., Zhou, T., Zhang, Q.-M., Stanley, H.E., The H-index of a network node and its relation to degree and coreness. Nature Commun., 6, 2016, 10168.
137. [137] Morone, F., Makse, H.A., Influence maximization in complex networks through optimal percolation. Nature 524 (2015), 65–68.
138. [138] Kim, H., Beznosov, K., Yoneki, E., Finding influential neighbors to maximize information diffusion in twitter. Proceedings of the 23rd ACM International Conference on World Wide Web, 2014, ACM Press, New York, 701–706.
139. [139] Liu, C., Zhan, X.-X., Zhang, Z.-K., Sun, G.-Q., Hui, P.M., How events determine spreading patterns: information transmission via internal and external influences on social networks. New J. Phys., 7, 2015, 113045.
140. [140] Yang, J., Counts, S., Predicting the speed, scale and range of information diffusion in twitter. Proceedings of the 4th AAAI International Conference on Weblogs and Social Media, 2010, AAAI Press, 355–358.
141. [141] Karsai, M., Iñiguez, G., Kaski, K., Kertész, J., Complex contagion process in spreading of online innovation. J. R. Soc. Interface, 11, 2014, 20140694.
142. [142] Kramer, A.D.I., Guillory, J.E., Hancock, J.T., Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. USA 111:24 (2014), 8788–8790.
143. [143] Barabási, A.-L., The origin of bursts and heavy tails in human dynamic. Nature 435 (2005), 207–211.
144. [144] Wu, Y., Zhou, C.-S., Xiao, J.-H., Kurths, J., Schellnhuber, H.J., Evidence for a bimodal distribution in human communication. Proc. Natl. Acad. Sci. USA 107:44 (2010), 18803–18808.
145. [145] Jiang, Z.-Q., Xie, W.-J., Li, M.-X., Podobnik, B., Zhou, W.-X., Stanley, H.E., Calling patterns in human communication dynamics. Proc. Natl. Acad. Sci. USA 110:5 (2013), 1600–1605.
146. [146] Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., Vespignani, A., Characterizing and modeling the dynamics of online popularity. Phys. Rev. Lett., 105, 2010, 158701.
147. [147] Vazquez, A., Rácz, B., Lukács, A., Barabási, A.-L., Impact of non-Poissonian activity patterns on spreading processes. Phys. Rev. Lett., 98, 2007, 158702.
148. [148] Min, B., Goh, K.-I., Vazquez, A., Spreading dynamics following bursty human activity patterns. Phys. Rev. E, 83, 2011, 036102.
149. [149] Iribarren, J.L., Moro, E., Branching dynamics of viral information spreading. Phys. Rev. E, 84, 2011, 046116.
150. [150] Backlund, V.-P., Saramäki, J., Pan, R.K., Effects of temporal correlations on cascades: threshold models on temporal networks. Phys. Rev. E, 89, 2014, 062815.
151. [151] Masuda, N., Klemm, K., Eguíluz, V.M., Temporal networks: slowing down diffusion by long lasting interactions. Phys. Rev. Lett., 111, 2013, 188701.
152. [152] Rocha, L.E.C., Liljeros, F., Holme, P., Simulated epidemics in an empirical spatiotemporal network of 50185 sexual contacts. PLoS Comput. Biol., 7(3), 2011, e1001109.
153. [153] Rocha, L.E.C., Blondel, V.D., Bursts of vertex activation and epidemics in evolving networks. PLoS Comput. Biol., 9(3), 2013, e1002974.
154. [154] Jo, H.-H., Perotti, J.I., Kaski, K., Kertész, J., Analytically solvable model of spreading dynamics with non-Poissonian processes. Phys. Rev. X, 4, 2014, 011041.
155. [155] Horváth, D.X., Kertész, J., Spreading dynamics on networks: the role of burstiness, topology and non-stationarity. New J. Phys., 16(7), 2014, 073037.
156. [156] Scholtes, I., Wider, N., Pfitzner, R., Garas, A., Tessone, C.J., Schweitzer, F., Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks. Nature Commun., 5, 2014, 5024.
157. [157] Gao, S., Ma, J., Chen, Z.-M., Modeling and predicting retweeting dynamics on microblogging platforms. Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 2015, ACM, New York, 107–116.
158. [158] Golder, S.A., Macy, M.W., Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333 (2011), 1878–1881.
159. [159] Malmgren, R.D., Stouffer, D.B., Campanharo, A.S.L.O., Amaral, L.A., On universality in human correspondence activity. Science 325 (2009), 1696–1700.
160. [160] Jo, H.-H., Karsai, M., Kertész, J., Kaski, K., Circadian pattern and burstiness in human communication activity. New J. Phys., 14, 2012, 013055.
161. [161] Dorogovtsev, S.N., Goltsev, A.V., Mendes, J.F., Critical phenomena in complex networks. Rev. Modern Phys. 80 (2008), 1275–1335.
162. [162] Buldyrev, S.V., Parshani, R., Paul, G., Stanley, H.E., Havlin, S., Catastrophic cascade of failures in interdependent networks. Nature 464 (2010), 1025–1028.
163. [163] Li, D.-Q., Fu, B.-W., Wang, Y.-P., Lu, G.-Q., Berezin, Y., Stanley, H.E., Havlin, S., Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proc. Natl. Acad. Sci. USA 112:3 (2015), 669–672.
164. [164] Parshani, R., Buldyrev, S.V., Havlin, S., Critical effect of dependency groups on the function of networks. Proc. Natl. Acad. Sci. USA 108:3 (2011), 1007–1010.
165. [165] Brummitt, C.D., D'Souza, R.M., Leicht, E.A., Suppressing cascades of load in interdependent networks. Proc. Natl. Acad. Sci. USA 109:12 (2012), 680–689.
166. [166] Weng, L.-L., Flammini, A., Vispignani, A., Menczer, F., Competition among memes in a world with limited attention. Sci. Rep., 2, 2012, 335.
167. [167] Gleeson, J.P., Ward, J.A., O'Sullivan, K.P., Lee, W.T., Competition-induced criticality in a model of meme popularity. Phys. Rev. Lett., 112, 2014, 048701.
168. [168] Yuan, Y., Li, B., Meng, M.Q.-H., Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J. Biomed. Health Inform. 20:2 (2016), 624–630.
169. [169] Yuan, H., Kwong, S., Wang, X., Zhang, Y., Li, F., A virtual view psnr estimation method for 3-d videos. IEEE Trans. Broadcast. 62:1 (2016), 134–140.
170. [170] Kosinski, M., Stillwell, D., Graepel, T., Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. USA 110:15 (2013), 5802–5805.
171. [171] Bhattacharya, P., Zafar, M.B., Ganguly, N., Ghosh, S., Gummadi, K.P., Inferring user interests in the twitter social network. Proceedings of the 8th ACM Conference on Recommender Systems, 2014, ACM Press, New York, 357–360.
172. [172] Mohammad, S.M., Kiritchenko, S., Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31:2 (2015), 301–326.
173. [173] Weng, L.-L., Menczer, F., Topicality and impact in social media: diverse message, focused messages. PLoS ONE, 10(2), 2015, e0118410.
174. [174] Zhang, J., Tang, J., Li, J.-Z., Liu, Y., Xing, C.-X., Who influenced you? Predicting retweet via social influence locality. ACM Trans. Knowl. Data Eng. D, 9(3), 2015, 25.
175. [175] Miotto, J.M., Altmann, E.G., Predictability of extreme events in social media. PLoS ONE, 9, 2014, e111506.
176. [176] Salganik, M.J., Dodds, P.S., Watts, D.J., Experimental study of inequality and unpredictability in an artificial cultural market. Science 311 (2006), 854–856.
177. [177] Holme, P., Takaguchi, T., Time evolution of predictability of epidemics on networks. Phys. Rev. E, 91(4), 2015, 042811.
178. [178] Holme, P., Information content of contact-pattern representations and predictability of epidemic outbreaks. Sci. Rep., 5, 2015, 14462.
179. [179] Cui, P., Jin, S.-F., Yu, L.-Y., Wang, F., Zhu, W.-W., Yang, S.-Q., Cascading outbreak prediction in networks: A data-driven approach. Proceedings of the 19th ACM SIGKDD International Conference on Konwledge Discovery and Data Mining, 2013, ACM Press, New York, 901–909.
180. [180] Wang, S.-Z., Yan, Z., Hu, X., Yu, P.S., Li, Z.-J., Wang, B., CPB: a classification-based approach for burst time prediction in cascades. Knowl. Inf. Syst., 2015, 1–29.
181. [181] Chen, D.-B., Xiao, R., Zeng, A., Predicting the evolution of spreading on complex networks. Sci. Rep., 4, 2014, 6108.
182. [182] Gomez-Rodriguez, M., Leskovec, J., Balduzzi, D., Schölkopf, B., Uncovering the structure and temporal dynamics of information propagation. Netw. Sci. 2 (2014), 26–65.
183. [183] D.B. Kurka, A. Godoy, F.J.V. Zuben, Online social network analysis: a survey of reaserch applications in computer science, 2015, arXiv:1504.05655.
184. [184] Li, Y., Liu, W., Huang, Q., Traffic anomaly detection based on image descriptor in videos. Multimedia Tools Appl. 75:5 (2016), 2487–2505.
185. [185] Yang, S.-H., Zha, H., Mixture of mutually exciting processes for viral diffusion. Proceedings of the 30th ACM International Conference on Machine Learning, 2013, ACM Press, New York, 1–9.
186. [186] Centola, D., Eguíluz, V.M., Macy, M.W., Cascade dynamics of complex propagation. Physica A 374:1 (2007), 449–456.
187. [187] Gleeson, J.P., Cahalane, D.J., Seed size strongly affects cascades on random networks. Phys. Rev. E, 75(5), 2007, 056103.
188. [188] Singh, P., Sreenivasan, S., Szymanski, B.K., Korniss, G., Threshold-limited spreading in social networks with multiple initiators. Sci. Rep., 3, 2013, 2330.
189. [189] Whitney, D.E., Dynamic theory of cascades on finite clustered random networks with a threshold rule. Phys. Rev. E, 82(6), 2010, 066110.
190. [190] Kempe, D., Kleinberg, J., Tardos, É, Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, ACM Press, New York, 137–146.
191. [191] Weng, L., Menczer, F., Ahn, Y.-Y., Virality prediction and community structure in social networks. Sci. Rep., 3, 2013, 2522.
192. [192] Dodds, P.S., Watts, D.J., Universal behavior in a generalized model of contagion. Phys. Rev. Lett., 92(21), 2004, 218701.
193. [193] Dodds, P.S., Watts, D.J., A generalized model of social and biological contagion. J. Theoret. Biol. 232:4 (2005), 587–604.
194. [194] Brummitt, C.D., Lee, K.-M., Goh, K.-I., Multiplexity-facilitated cascades in networks. Phys. Rev. E, 85(4), 2012, 045102.
195. [195] Yağan, O., Gligor, V., Analysis of complex contagions in random multiplex networks. Phys. Rev. E, 86(3), 2012, 036103.
196. [196] Takaguchi, T., Masuda, N., Holme, P., Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics. PloS ONE, 8(7), 2013, e68629.
197. [197] Karimi, F., Holme, P., Threshold model of cascades in empirical temporal networks. Physica A 392:16 (2013), 3476–3483.
198. [198] Wang, W., Tang, M., Zhang, H.-F., Lai, Y.-C., Dynamics of social contagions with memory of nonredundant information. Phys. Rev. E, 92(1), 2015, 012820.
199. [199] Wang, W., Tang, M., Shu, P., Wang, Z., Dynamics of social contagions with heterogeneous adoption thresholds: crossover phenomena in phase transition. New J. Phys., 18(1), 2016, 013029.
200. [200] Liggett, T., Interacting Particle Systems. 2012, Springer Science & Business Media.
201. [201] Goldenberg, J., Libai, B., Muller, E., Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad. Market. Sci. Rev., 2001, 2001, 9.
202. [202] Saito, K., Nakano, R., Kimura, M., Prediction of information diffusion probabilities for independent cascade model. Proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 2008, Springer, 67–75.
203. [203] Dickens, L., Molloy, I., Lobo, J., Cheng, P.-C., Russo, A., Learning stochastic models of information flow. Proceedings of 28th IEEE International Conference on Data Engineering, 2012, IEEE Press, 570–581.
204. [204] Kimura, M., Saito, K., Nakano, R., Extracting influential nodes for information diffusion on a social network. Proceedings of the 22nd AAAI International Conference on Artificial intelligence, 2007, AAAI Press, 1371–1376.
205. [205] Kimura, M., Saito, K., Nakano, R., Motoda, H., Finding influential nodes in a social network from information diffusion data. Social Computing and Behavioral Modeling, 2009, Springer, 1–8.
206. [206] Kimura, M., Saito, K., Motoda, H., Minimizing the spread of contamination by blocking links in a network. Proceedings of 23rd AAAI Conference on Artificial Intelligence, 2008, AAAI Press, 1175–1180.
207. [207] Albert, R., Jeong, H., Barabási, A.-L., Error and attack tolerance of complex networks. Nature 406:6794 (2000), 378–382.
208. [208] Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J., Graph structure in the web. Comput. Netw. 33:1 (2000), 309–320.
209. [209] Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A., Information diffusion through blogspace. Proceedings of the 13th ACM International Conference on World Wide Web, 2004, ACM Press, New York, 491–501.
210. [210] Saito, K., Kimura, M., Ohara, K., Motoda, H., Learning continuous-time information diffusion model for social behavioral data analysis. Advances in Machine Learning, 2009, Springer, 322–337.
211. [211] Saito, K., Kimura, M., Ohara, K., Motoda, H., Selecting information diffusion models over social networks for behavioral analysis. Machine Learning and Knowledge Discovery in Databases, 2010, Springer, 180–195.
212. [212] Guille, A., Hacid, H., A predictive model for the temporal dynamics of information diffusion in online social networks. Proceedings of the 21st ACM International Conference on World Wide Web, 2012, ACM Press, New York, 1145–1152.
213. [213] Kempe, D., Kleinberg, J., Tardos, É, Influential nodes in a diffusion model for social networks. Automata, Languages and Programming, 2005, Springer, 1127–1138.
214. [214] Barbieri, N., Bonchi, F., Manco, G., Topic-aware social influence propagation models. Knowl. Inf. Syst. 37:3 (2013), 555–584.
215. [215] Kermack, W.O., McKendrick, A.G., A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 115:772 (1927), 700–721.
216. [216] Kermack, W., McKendrick, A., Contributions to the mathematical theory of epidemics ii. The problem of endemicity. Bull. Math. Biol. 53:1–2 (1991), 57–87.
217. [217] Heffernan, J., Smith, R., Wahl, L., Perspectives on the basic reproductive ratio. J. R. Soc. Interface 2:4 (2005), 281–293.
218. [218] Anderson, R.M., May, R.M., Infectious Diseases of Humans: Dynamics and Control. 1991, Oxford University Press.
219. [219] Barrat, A., Barthelemy, M., Vespignani, A., Dynamical Processes on Complex Networks. 2008, Cambridge University Press.
220. [220] Moreno, Y., Pastor-Satorras, R., Vespignani, A., Epidemic outbreaks in complex heterogeneous networks. Eur. Phys. J. B 26:4 (2002), 521–529.
221. [221] Yang, R., Wang, B.-H., Ren, J., Bai, W.-J., Shi, Z.-W., Wang, W.-X., Zhou, T., Epidemic spreading on heterogeneous networks with identical infectivity. Phys. Lett. A 364:3 (2007), 189–193.
222. [222] Boguná, M., Pastor-Satorras, R., Vespignani, A., et al. Epidemic spreading in complex networks with degree correlations. Proceedings of the 18th Sitges Conference on Statistical Mechanics Lecture Notes in Physics, 2003, Springer-Verlag, Berlin.
223. [223] Morris, A.J., Representing spatial interactions in simple ecological models. (Ph.D. thesis), 1997, University of Warwick.
224. [224] Keeling, M.J., The effects of local spatial structure on epidemiological invasions. Proc. R. Soc. Lond. B: Biol. Sci. 266:1421 (1999), 859–867.
225. [225] House, T., Keeling, M.J., Insights from unifying modern approximations to infections on networks. J. R. Soc. Interface 8:54 (2011), 67–73.
226. [226] Youssef, M., Scoglio, C., An individual-based approach to sir epidemics in contact networks. J. Theoret. Biol. 283:1 (2011), 136–144.
227. [227] Van~Mieghem, P., Omic, J., Kooij, R., Virus spread in networks. IEEE ACM Trans. Netw. 17:1 (2009), 1–14.
228. [228] Newman, M.E., Strogatz, S.H., Watts, D.J., Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E, 64(2), 2001, 026118.
229. [229] Hamilton, K.E., Pryadko, L.P., Tight lower bound for percolation threshold on an infinite graph. Phys. Rev. Lett., 113(20), 2014, 208701.
230. [230] Karrer, B., Newman, M.E., Zdeborová, L., Percolation on sparse networks. Phys. Rev. Lett., 113(20), 2014, 208702.
231. [231] Yang, J., Leskovec, J., Modeling information diffusion in implicit networks. Proceedings of the 2010 IEEE International Conference on Data Mining, 2010, IEEE Press, 599–608.
232. [232] Wang, F., Wang, H., Xu, K., Diffusive logistic model towards predicting information diffusion in online social networks. Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops, 2012, IEEE Press, 133–139.
233. [233] Kleineberg, K.-K., Boguñá, M., Evolution of the digital society reveals balance between viral and mass media influence. Phys. Rev. X, 4(3), 2014, 031046.
234. [234] X.-X. Zhan, C. Liu, G. Zhou, Z.-K. Zhang, G.-Q. Sun, J.J. Zhu, Mutual feedback between epidemic spreading and information diffusion, 2015, arXiv:1506.03932.
235. [235] Funk, S., Gilad, E., Jansen, V., Endemic disease, awareness, and local behavioural response. J. Theoret. Biol. 264:2 (2010), 501–509.
236. [236] Funk, S., Salathé, M., Jansen, V.A., Modelling the influence of human behaviour on the spread of infectious diseases: a review. J. R. Soc. Interface 7:50 (2010), 1247–1256.
237. [237] X.-X. Zhan, C. Liu, G.-Q. Sun, Z.-K. Zhang, Epidemic dynamics on information-driven adaptive networks, 2015, arXiv:1505.04856.
238. [238] Morris, S., Contagion. Rev. Econ. Stud. 67:1 (2000), 57–78.
239. [239] Even-Dar, E., Shapira, A., A note on maximizing the spread of influence in social networks. Internet and Network Economics, 2007, Springer, 281–286.
240. [240] Ehrhardt, G.C., Marsili, M., Vega-Redondo, F., Phenomenological models of socioeconomic network dynamics. Phys. Rev. E, 74(3), 2006, 036106.
241. [241] Benczik, I., Benczik, S., Schmittmann, B., Zia, R., Lack of consensus in social systems. Europhys. Lett., 82(4), 2008, 48006.
242. [242] Caldarelli, G., Chessa, A., Pammolli, F., Gabrielli, A., Puliga, M., Reconstructing a credit network. Nat. Phys. 9:3 (2013), 125–126.
243. [243] Gardner, T.S., Di~Bernardo, D., Lorenz, D., Collins, J.J., Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301:5629 (2003), 102–105.
244. [244] Timme, M., Revealing network connectivity from response dynamics. Phys. Rev. Lett., 98(22), 2007, 224101.
245. [245] Hempel, S., Koseska, A., Kurths, J., Nikoloski, Z., Inner composition alignment for inferring directed networks from short time series. Phys. Rev. Lett., 107(5), 2011, 054101.
246. [246] Levnajić, Z., Pikovsky, A., Network reconstruction from random phase resetting. Phys. Rev. Lett., 107(3), 2011, 034101.
247. [247] Clauset, A., Moore, C., Newman, M.E., Hierarchical structure and the prediction of missing links in networks. Nature 453:7191 (2008), 98–101.
248. [248] Bongard, J., Lipson, H., Automated reverse engineering of nonlinear dynamical systems. Proc. Natl. Acad. Sci. USA 104:24 (2007), 9943–9948.
249. [249] Albert, R., Barabási, A.-L., Statistical mechanics of complex networks. Rev. Modern Phys., 74(1), 2002, 47.
250. [250] Newman, M.E., The structure and function of complex networks. SIAM Rev. 45:2 (2003), 167–256.
251. [251] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U., Complex networks: Structure and dynamics. Phys. Rep. 424:4 (2006), 175–308.
252. [252] Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V., Grebogi, C., Predicting catastrophes in nonlinear dynamical systems by compressive sensing. Phys. Rev. Lett., 106(15), 2011, 154101.
253. [253] Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V., Harrison, M.A.F., Time-series–based prediction of complex oscillator networks via compressive sensing. Europhys. Lett., 94(4), 2011, 48006.
254. [254] Wang, W.-X., Lai, Y.-C., Grebogi, C., Ye, J., Network reconstruction based on evolutionary-game data via compressive sensing. Phys. Rev. X, 1(2), 2011, 021021.
255. [255] Candès, E.J., Romberg, J., Tao, T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 52:2 (2006), 489–509.
256. [256] Candes, E.J., Romberg, J.K., Tao, T., Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59:8 (2006), 1207–1223.
257. [257] Donoho, D.L., Compressed sensing. IEEE Trans. Inform. Theory 52:4 (2006), 1289–1306.
258. [258] Candè, E.J., Wakin, M.B., An introduction to compressive sampling. IEEE Trans. Signal Process. 25:2 (2008), 21–30.
259. [259] Pastor-Satorras, R., Vespignani, A., Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86(14), 2001, 3200.
260. [260] Castellano, C., Pastor-Satorras, R., Non-mean-field behavior of the contact process on scale-free networks. Phys. Rev. Lett., 96(3), 2006, 038701.
261. [261] Gomez~Rodriguez, M., Leskovec, J., Krause, A., Inferring networks of diffusion and influence. Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data Mining, 2010, ACM Press, New York, 1019–1028.
262. [262] Gomez~Rodriguez, M., Leskovec, J., Schölkopf, B., Structure and dynamics of information pathways in online media. Proceedings of the 6th ACM International Conference on Web Search and Data Mining, 2013, ACM Press, New York, 23–32.
263. [263] Lokhov, A.Y., Mézard, M., Ohta, H., Zdeborová, L., Inferring the origin of an epidemic with a dynamic message-passing algorithm. Phys. Rev. E, 90(1), 2014, 012801.
264. [264] Tripathy, R.M., Bagchi, A., Mehta, S., Towards combating rumors in social networks: Models and metrics. Intell. Data Anal. 17:1 (2013), 149–175.
265. [265] Daley, D.J., Kendall, D.G., Epidemics and rumours. Nature, 204, 1964, 1118.
266. [266] Daley, D., Kendall, D.G., Stochastic rumours. IMA J. Appl. Math. 1:1 (1965), 42–55.
267. [267] Maki, D.P., Thompson, M., Mathematical Models and Applications, with Emphasis on the Social, Life and Management Sciences. 1973, Prentice Hall.
268. [268] D.H. Zanette, Criticality of rumor propagation on small-world networks, 2001, arXiv:cond-mat/0109049.
269. [269] Zanette, D.H., Dynamics of rumor propagation on small-world networks. Phys. Rev. E, 65(4), 2002, 041908.
270. [270] Moreno, Y., Nekovee, M., Pacheco, A.F., Dynamics of rumor spreading in complex networks. Phys. Rev. E, 69(6), 2004, 066130.
271. [271] Moreno, Y., Nekovee, M., Vespignani, A., Efficiency and reliability of epidemic data dissemination in complex networks. Phys. Rev. E, 69(5), 2004, 055101.
272. [272] Tripathy, R.M., Bagchi, A., Mehta, S., A study of rumor control strategies on social networks. Proceedings of the 19th ACM International Conference on Information and knowledge management, 2010, ACM Press, New York, 1817–1820.
273. [273] Budak, C., Agrawal, D., El~Abbadi, A., Limiting the spread of misinformation in social networks. Proceedings of the 20th International Conference on World wide Web, 2011, ACM Press, New York, 665–674.
274. [274] Borge-Holthoefer, J., Moreno, Y., Absence of influential spreaders in rumor dynamics. Phys. Rev. E, 85(2), 2012, 026116.
275. [275] Borge-Holthoefer, J., Rivero, A., Moreno, Y., Locating privileged spreaders on an online social network. Phys. Rev. E, 85(6), 2012, 066123.
276. [276] Aral, S., Walker, D., Identifying influential and susceptible members of social networks. Science 337:6092 (2012), 337–341.
277. [277] Valente, T.W., Davis, R.L., Accelerating the diffusion of innovations using opinion leaders. Ann. Am. Acad. Pol. Soc. Sci. 566:1 (1999), 55–67.
278. [278] Watts, D.J., Strogatz, S.H., Collective dynamics of small-worldnetworks. Nature 393:6684 (1998), 440–442.
279. [279] Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., Zhou, T., Identifying influential nodes in complex networks. Physica A 391:4 (2012), 1777–1787.
280. [280] Ghoshal, G., Barabási, A.-L., Ranking stability and super-stable nodes in complex networks. Nature Commun., 2, 2011, 394.
281. [281] Lou, T., Tang, J., Mining structural hole spanners through information diffusion in social networks. Proceedings of the 22nd ACM International Conference on World Wide Web, 2013, ACM Press, New York, 825–836.
282. [282] Lü, L., Zhang, Y.-C., Yeung, C.H., Zhou, T., Leaders in social networks, the delicious case. PloS ONE, 6(6), 2011, e21202.
283. [283] Tang, J., Sun, J., Wang, C., Yang, Z., Social influence analysis in large-scale networks. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, ACM Press, New York, 807–816.
284. [284] Yang, J., Counts, S., Predicting the speed, scale, and range of information diffusion in twitter. Proceedings of the 4th International Conference on Weblogs and Social Media, 2010, AAAI Press, 355–358.
285. [285] Gilbert, M., Golding, N., Zhou, H., Wint, G.W., Robinson, T.P., Tatem, A.J., Lai, S., Zhou, S., Jiang, H., Guo, D., et al. Predicting the risk of avian influenza a h7n9 infection in live-poultry markets across asia. Nature Commun., 5, 2014, 4116.
286. [286] Hethcote, H.W., The mathematics of infectious diseases. SIAM Rev. 42:4 (2000), 599–653.
287. [287] Kleinberg, J., Computing: The wireless epidemic. Nature 449:7160 (2007), 287–288.
288. [288] B.A. Prakash, D. Chakrabarti, M. Faloutsos, N. Valler, C. Faloutsos, Threshold conditions for arbitrary cascade models on arbitrary networks, in: Proceedings of the 11th IEEE International Conference on Data Mining, IEEE process, 2011, pp. 537–546.
289. [289] Briesemeister, L., Lincoln, P., Porras, P., Epidemic profiles and defense of scale-free networks. Proceedings of the 2003 ACM Workshop on Rapid Malcode, 2003, ACM Press, New York, 67–75.
290. [290] Y. Hayashi, M. Minoura, J. Matsukubo, Recoverable prevalence in growing scale-free networks and the effective immunization, 2003, arXiv:cond-mat/0305549.
291. [291] Prakash, B.A., Tong, H., Valler, N., Faloutsos, M., Faloutsos, C., Virus propagation on time-varying networks: Theory and immunization algorithms. Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases, 2010, Springer Press, 99–114.
292. [292] Valler, N.C., Prakash, B.A., Tong, H., Faloutsos, M., Faloutsos, C., Epidemic spread in mobile ad hoc networks: Determining the tipping point. Proceedings of the 10th International IFIP TC 6 Conference on Networking, 2011, Springer Press, 266–280.
293. [293] Marcelino, J., Kaiser, M., Reducing influenza spreading over the airline network. PLoS Curr. Influenza, 1, 2009, RRN1005.
294. [294] C.M. Schneider, T. Mihaljev, S. Havlin, H.J. Herrmann, Restraining epidemics by improving immunization strategies, 2011, arXiv:1102.1929.
295. [295] Holme, P., Kim, B.J., Yoon, C.N., Han, S.K., Attack vulnerability of complex networks. Phys. Rev. E, 65(5), 2002, 056109.
296. [296] Kuhlman, C.J., Tuli, G., Swarup, S., Marathe, M.V., Ravi, S., Blocking simple and complex contagion by edge removal. Proceedings of the 13th IEEE International Conference on Data Mining, 2013, IEEE Press, 399–408.
297. [297] Bishop, A.N., Shames, I., Link operations for slowing the spread of disease in complex networks. Europhys. Lett., 95(1), 2011, 18005.
298. [298] Chan, H., Akoglu, L., Tong, H., Make it or break it: Manipulating robustness in large networks. Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, SIAM, 325–333.
299. [299] Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T., Recommender systems. Phys. Rep. 519:1 (2012), 1–49.