Interdependent Networks from Societal Perspective: MITS (Multi-Context Influence Tracking on Social Network)

  • Ramesh BaralEmail author
  • S. S. Iyengar
  • Asad M. Madni
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 186)


The real-world system can be represented in terms of multiple complex and semantically coherent networks. The networks have some correlation among each other and complement each other’s functionality. Such correlated networks are termed as interdependent networks. The notion of a smart city can be represented as an integration of several interdependent networks that can facilitate secured and efficient management of a city’s assets, such as transportation, power grids, water supply channel, distributed sensor networks, societal networks, and other services. In this chapter, we introduce the societal perspective of interdependent networks, where the users’ and locations’ networks are exploited to track the influential user and location nodes.

The task of identifying and tracking influential nodes in the ever-growing information networks is crucial to real-world problems that require information propagation (e.g., viral marketing). The exploitation of social networks for influential node detection has been quite popular in the last decade. However, most of the studies have focused on networks with homogeneous nodes (e.g., user-user nodes), and have also ignored the impact of relevant contexts. The information networks have heterogeneous entities that are interconnected and complement each other’s functionality. Hence, the classical techniques popular in modeling the spreading of epidemics in simple networks may not be efficient.

We propose a model called MITS (Multi-context Influence Tracking on Social Network) that represents the contextual exploitation of heterogeneous nodes (i.e., user-location nodes in Location-based Social Networks (LBSN)), formulates the locality-aware spatial-socio-temporal influence tracking problem using Brooks-Iyengar hybrid algorithm, and uses the geo-tagged check-in data to identify and track the locality influence. The empirical evaluation of the proposed model on two real-world datasets, using the Susceptible-Infected-Recovered (SIR) epidemic technique, coverage, and ratio of affection metrics demonstrates a significant performance gain (e.g., 10–85% on coverage and 14–39% on ratio of affection) of the proposed model against other popular techniques, such as degree centrality, betweenness centrality, closeness centrality, and PageRank.



This research is partially supported by US Army Research Lab under the grant number W911NF-12-R-0012 and by FIU Dissertation Year Fellowship.


  1. 1.
    Amini, M. H., Boroojeni, K. G., Iyengar, S., Pardalos, P. M., Blaabjerg, F., & Madni, A. M. (2018). Sustainable interdependent networks: From theory to application. Basel: Springer.Google Scholar
  2. 2.
    Cocchia, A. (2014). Smart and digital city: A systematic literature review. In Smart city (pp. 13–43). Basel: Springer.Google Scholar
  3. 3.
    Hall, R. E., Bowerman, B., Braverman, J., Taylor, J., Todosow, H., & Von Wimmersperg, U. (2000). The Vision of a Smart City, Brookhaven National Lab., Upton, NY (US). Technical Report.Google Scholar
  4. 4.
    Lombardi, P., Giordano, S., Farouh, H., & Yousef, W. (2012). Modelling the smart city performance. Innovation: The European Journal of Social Science Research, 25(2), 137–149.Google Scholar
  5. 5.
    Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., et al. (2016). Iot-based smart cities: a survey. In 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) (pp. 1–6). New York: IEEE.Google Scholar
  6. 6.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gharani, P., & Karimi, H. A. (2017). Context-aware obstacle detection for navigation by visually impaired. Image and Vision Computing, 64, 103–115.CrossRefGoogle Scholar
  8. 8.
    Scott, J., & Carrington, P. J. (2011). The SAGE handbook of social network analysis. Thousand Oaks: SAGE Publications.Google Scholar
  9. 9.
    Aiello, W., Chung, F., & Lu, L. (2002). Random evolution in massive graphs. In Handbook of massive data sets (pp. 97–122). New York: Springer.CrossRefGoogle Scholar
  10. 10.
    Caldarelli, G., Coccetti, F., & De Los Rios, P. (2004). Preferential exchange: Strengthening connections in complex networks. Physical Review E, 70(2), p. 027102.Google Scholar
  11. 11.
    Price, D. J. D. S. (1965). Networks of scientific papers. Science, 30, 510–515.CrossRefGoogle Scholar
  12. 12.
    Amini, M. H., & Karabasoglu, O. (2018). Optimal operation of interdependent power systems and electrified transportation networks. Energies, 11(1), 196.CrossRefGoogle Scholar
  13. 13.
    Vogelstein, B., Lane, D., & Levine, A. J. (2000). Surfing the p53 network. Nature, 408(6810), 307.CrossRefGoogle Scholar
  14. 14.
    Sompolinsky, H., Crisanti, A., & Sommers, H.-J. (1988). Chaos in random neural networks. Physical Review Letters, 61(3), 259.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Minai, A. A., & Levy, W. B. (1993). The dynamics of sparse random networks. Biological Cybernetics, 70(2), 177–187.CrossRefGoogle Scholar
  16. 16.
    Mari, C. F. (2000). Random networks of spiking neurons: Instability in the xenopus tadpole moto-neural pattern. Physical Review Letters, 85(1), 210.CrossRefGoogle Scholar
  17. 17.
    Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical Review E, 72(2), 027104.CrossRefGoogle Scholar
  18. 18.
    Mistani, P., Guittet, A., Bochkov, D., Schneider, J., Margetis, D., Ratsch, C., & Gibou, F. (2018). The island dynamics model on parallel quadtree grids. Journal of Computational Physics, 361, 150–166.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268.CrossRefGoogle Scholar
  20. 20.
    Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 1025.CrossRefGoogle Scholar
  21. 21.
    Parshani, R., Buldyrev, S. V., & Havlin, S. (2010). Interdependent networks: Reducing the coupling strength leads to a change from a first to second order percolation transition. Physical Review Letters, 105(4), 048701.CrossRefGoogle Scholar
  22. 22.
    Buldyrev, S. V., Shere, N. W., & Cwilich, G. A. (2011). Interdependent networks with identical degrees of mutually dependent nodes. Physical Review E, 83(1), 016112.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Phadke, A., & Thorp, J. S. (1996). Expose hidden failures to prevent cascading outages [in power systems]. IEEE Computer Applications in Power, 9(3), 20–23.CrossRefGoogle Scholar
  24. 24.
    Reis, S. D., Hu, Y., Babino, A., Andrade, J. S. Jr., Canals, S., Sigman, M., et al. (2014). Avoiding catastrophic failure in correlated networks of networks. Nature Physics, 10(10), 762.CrossRefGoogle Scholar
  25. 25.
    Dobson, I., Carreras, B. A., Lynch, V. E., & Newman, D. E. (2007). Complex systems analysis of series of blackouts: Cascading failure, critical points, and self-organization. Chaos: An Interdisciplinary Journal of Nonlinear Science, 17(2), 026103.CrossRefGoogle Scholar
  26. 26.
    Gao, J., Buldyrev, S. V., Stanley, H. E., & Havlin, S. (2012). Networks formed from interdependent networks. Nature Physics, 8(1), 40.CrossRefGoogle Scholar
  27. 27.
    Huang, X., Gao, J., Buldyrev, S. V., Havlin, S., & Stanley, H. E. (2011). Robustness of interdependent networks under targeted attack. Physical Review E, 83(6), 065101.CrossRefGoogle Scholar
  28. 28.
    Schneider, C. M., Yazdani, N., Araújo, N. A. , Havlin, S., & Herrmann, H. J. (2013). Towards designing robust coupled networks. Scientific Reports, 3, 1969.CrossRefGoogle Scholar
  29. 29.
    Gao, J., Buldyrev, S. V., Havlin, S., & Stanley, H. E. (2012). Robustness of a network formed by n interdependent networks with a one-to-one correspondence of dependent nodes. Physical Review E, 85(6), 066134.CrossRefGoogle Scholar
  30. 30.
    Dong, G., Gao, J., Du, R., Tian, L., Stanley, H. E., & Havlin, S. (2013). Robustness of network of networks under targeted attack. Physical Review E, 87(5), 052804.CrossRefGoogle Scholar
  31. 31.
    Bollobás, B. (1998). Random graphs. In Modern graph theory (pp. 215–252). New York: Springer.CrossRefGoogle Scholar
  32. 32.
    West, D. B., et al. (2001). Introduction to graph theory (Vol. 2). Upper Saddle River: Prentice Hall.Google Scholar
  33. 33.
    Bollobás, B. (2013). Modern graph theory (Vol. 184). Berlin/Heidelberg: Springer Science & Business Media.Google Scholar
  34. 34.
    Cohen, R., Erez, K., Ben-Avraham, D., & Havlin, S. (2001). Breakdown of the internet under intentional attack. Physical Review Letters, 86(16), 3682.CrossRefGoogle Scholar
  35. 35.
    Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical Review Letters, 86(14), 3200.CrossRefGoogle Scholar
  36. 36.
    Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  37. 37.
    Friedkin, N. E. (1991). Theoretical foundations for centrality measures. American Journal of Sociology, 96(6), 1478–1504.CrossRefGoogle Scholar
  38. 38.
    Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603.MathSciNetCrossRefGoogle Scholar
  39. 39.
    Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., et al. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888.CrossRefGoogle Scholar
  40. 40.
    Freeman, L. C. (1996). Some antecedents of social network analysis. Connections, 19(1), 39–42.Google Scholar
  41. 41.
    Wellman, B. (1926). The school child’s choice of companions. The Journal of Educational Research, 14(2), 126–132.CrossRefGoogle Scholar
  42. 42.
    Erds, P., & Rényi, A. (1960). On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.MathSciNetGoogle Scholar
  43. 43.
    R. M. May, Stability and complexity in model ecosystems. Princeton university press, 2001, vol. 6.Google Scholar
  44. 44.
    Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22(3), 437–467.MathSciNetCrossRefGoogle Scholar
  45. 45.
    Albert, R., Jeong, H., & Barabási, A. -L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378.CrossRefGoogle Scholar
  46. 46.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407(6804), 651.CrossRefGoogle Scholar
  47. 47.
    Wagner, A., & Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of London B: Biological Sciences, 268, 1478, pp. 1803–1810.CrossRefGoogle Scholar
  48. 48.
    Newman, M. E. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.MathSciNetCrossRefGoogle Scholar
  49. 49.
    Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 137–146). New York: ACM.CrossRefGoogle Scholar
  50. 50.
    Marzullo, K. (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer Systems (TOCS) (Vol. 8(4), pp. 284–304).Google Scholar
  51. 51.
    Sahni, S., & Xu, X. (2005). Algorithms for wireless sensor networks. International Journal of Distributed Sensor Networks, 1(1), 35–56.CrossRefGoogle Scholar
  52. 52.
    Ao, B., Wang, Y., Yu, L., Brooks, R. R., & Iyengar, S. (2016). On precision bound of distributed fault-tolerant sensor fusion algorithms. ACM Computing Surveys (CSUR), 49(1), 5.CrossRefGoogle Scholar
  53. 53.
    Wu, H.-H., & Yeh, M.-Y. (2013). Influential nodes in a one-wave diffusion model for location-based social networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 61–72). New York: Springer.CrossRefGoogle Scholar
  54. 54.
    Zhang, C., Shou, L., Chen, K., Chen, G., & Bei, Y. (2012). Evaluating geo-social influence in location-based social networks. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 1442–1451). New York: ACM.Google Scholar
  55. 55.
    Zhu, W.-Y., Peng, W.-C., Chen, L.-J., Zheng, K., & Zhou, X. (2015). Modeling user mobility for location promotion in location-based social networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1573–1582). New York: ACM.CrossRefGoogle Scholar
  56. 56.
    Domingos, P., & Richardson, M. (2001). Mining the network value of customers. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 57–66). New York: ACM.CrossRefGoogle Scholar
  57. 57.
    Brooks, R. R., & Iyengar, S. S. (1996). Robust distributed computing and sensing algorithm. Computer, 29(6), 53–60.CrossRefGoogle Scholar
  58. 58.
    Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.CrossRefGoogle Scholar
  59. 59.
    Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.CrossRefGoogle Scholar
  60. 60.
    Lü, L., Zhang, Y.-C., Yeung, C. H., & Zhou, T. (2011). Leaders in social networks, the delicious case. PLoS One, 6(6), e21202.CrossRefGoogle Scholar
  61. 61.
    Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.CrossRefGoogle Scholar
  62. 62.
    Radicchi, F., Fortunato, S., Markines, B., & Vespignani, A. (2009). Diffusion of scientific credits and the ranking of scientists. Physical Review E, 80(5), 056103.CrossRefGoogle Scholar
  63. 63.
    Lee, S. H., Kim, P.-J., Ahn, Y.-Y., & Jeong, H. (2010). Googling social interactions: Web search engine based social network construction. PLoS One, 5(7), e11233.CrossRefGoogle Scholar
  64. 64.
    Zeng, A., & Zhang, C.-J. (2013). Ranking spreaders by decomposing complex networks. Physics Letters A, 377(14), 1031–1035.CrossRefGoogle Scholar
  65. 65.
    Barbieri, N., Bonchi, F., & Manco, G. (2012). Topic-aware social influence propagation models. In 2012 IEEE 12th International Conference on Data Mining (ICDM) (pp. 81–90). New York: IEEE.CrossRefGoogle Scholar
  66. 66.
    Zhu, W.-Y., Peng, W.-C., Chen, L.-J., Zheng, K., & Zhou, X. (2016). Exploiting viral marketing for location promotion in location-based social networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(2), 25.CrossRefGoogle Scholar
  67. 67.
    Wang, Y., Cong, G., Song, G., & Xie, K. (2010). Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1039–1048). New York: ACM.CrossRefGoogle Scholar
  68. 68.
    Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and Its Applications, 391(4), 1777–1787.CrossRefGoogle Scholar
  69. 69.
    Zhang, X., Zhu, J., Wang, Q., & Zhao, H. (2013). Identifying influential nodes in complex networks with community structure. Knowledge-Based Systems, 42, 74–84.CrossRefGoogle Scholar
  70. 70.
    Li, G., Chen, S., Feng, J., Tan, K.-L., & Li, W.-S. (2014). Efficient location-aware influence maximization. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (pp. 87–98). New York: ACM.Google Scholar
  71. 71.
    Malliaros, F. D., Rossi, M.-E. G., & Vazirgiannis, M. (2016). Locating influential nodes in complex networks. Scientific Reports, 6, 19307.CrossRefGoogle Scholar
  72. 72.
    Wang, Z., Du, C., Fan, J., & Xing, Y. (2017). Ranking influential nodes in social networks based on node position and neighborhood. Neurocomputing, 260, 466–477.CrossRefGoogle Scholar
  73. 73.
    Wang, Z., Zhao, Y., Xi, J., & Du, C. (2016). Fast ranking influential nodes in complex networks using a k-shell iteration factor. Physica A: Statistical Mechanics and Its Applications, 461, 171–181.CrossRefGoogle Scholar
  74. 74.
    Baral, R., Wang, D., Li, T., & Chen, S.-C. (2016). Geotecs: Exploiting geographical, temporal, categorical and social aspects for personalized POI recommendation. In 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) (pp. 94–101). New York: IEEE.CrossRefGoogle Scholar
  75. 75.
    Liu, Y., Wei, W., Sun, A., & Miao, C. (2014). Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 739–748). New York: ACM.Google Scholar

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Authors and Affiliations

  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.Department of Electrical & Computer EngineeringUniversity of California Los AngelesLos AngelesUSA

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