Networks and Context: Algorithmic Challenges for Context-Aware Social Network Research

  • Mirco SchoenfeldEmail author
  • Juergen Pfeffer
Part of the Lecture Notes in Social Networks book series (LNSN)


Social interaction is mediated by computer processes at an ever-increasing rate not least because more and more people have smartphones as their everyday and habitual companions. This enables collection of a vast amount of data containing an unprecedented richness of metadata of interaction and communication. Such context information contains valuable insights for social network research and allows for qualitative grading of network structure and consecutive structural analysis. Due to the complex, heterogeneous, dynamic, and uncertain nature of such information it is yet to be considered for network analysis tasks in its entirety. In this paper, we emphasize how network analysis benefits from considering context information and identify the key challenges that have to be tackled. From an algorithmic perspective, such challenges appear on all steps of a network analysis workflow: Dynamics and uncertainty of information affects modeling networks, calculation of general metrics, calculation of centrality rankings, graph clustering, and visualization. Ultimately, novel algorithms have to be designed to combine context data and structural information to enable future context-aware network research.


Context awareness Social networks Network research 


  1. 1.
    Battiston, F., Nicosia, V., Latora, V.: Structural measures for multiplex networks. Phys. Rev. E 89, 032804 (2014). CrossRefGoogle Scholar
  2. 2.
    Beck, F., Burch, M., Diehl, S., Weiskopf, D.: A taxonomy and survey of dynamic graph visualization. Comput. Graphics Forum 36(1), 133–159 (2017). CrossRefGoogle Scholar
  3. 3.
    Bello, G.A., Harenberg, S., Agrawal, A., Samatova, N.F.: Community detection in dynamic attributed graphs. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) Advanced Data Mining and Applications, pp. 329–344. Springer, Cham (2016)CrossRefGoogle Scholar
  4. 4.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). zbMATHCrossRefGoogle Scholar
  5. 5.
    Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., Zanin, M.: The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122 (2014). MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bojchevski, A., Günnemann, S.: Bayesian robust attributed graph clustering: joint learning of partial anomalies and group structure. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  7. 7.
    Bothorel, C., Cruz, J.D., Magnani, M., Magnani, B.: Clustering attributed graphs: models, measures and methods. Netw. Sci. 3(3), 408–444 (2015). CrossRefGoogle Scholar
  8. 8.
    Bu, Z., Gao, G., Li, H.J., Cao, J.: CAMAS: a cluster-aware multiagent system for attributed graph clustering. Inform. Fusion 37, 10–21 (2017). CrossRefGoogle Scholar
  9. 9.
    Cheng, H., Zhou, Y., Yu, J.X.: Clustering large attributed graphs: a balance between structural and attribute similarities. ACM Trans. Knowl. Discov. Data 5(2), 12:1–12:33 (2011). MathSciNetCrossRefGoogle Scholar
  10. 10.
    De Bacco, C., Power, E.A., Larremore, D.B., Moore, C.: Community detection, link prediction, and layer interdependence in multilayer networks. Phys. Rev. E 95, 042317 (2017). CrossRefGoogle Scholar
  11. 11.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001). CrossRefGoogle Scholar
  12. 12.
    Domenico, M.D., Porter, M.A., Arenas, A.: MuxViz: a tool for multilayer analysis and visualization of networks. J. Complex Netw. 3(2), 159–176 (2015). CrossRefGoogle Scholar
  13. 13.
    Du, Y., Gao, C., Chen, X., Hu, Y., Sadiq, R., Deng, Y.: A new closeness centrality measure via effective distance in complex networks. Chaos Interdiscip. J. Nonlinear Sci. 25(3), 033112 (2015). MathSciNetCrossRefGoogle Scholar
  14. 14.
    Günnemann, S., Färber, I., Raubach, S., Seidl, T.: Spectral subspace clustering for graphs with feature vectors. In: 2013 IEEE 13th International Conference on Data Mining, pp. 231–240 (2013).
  15. 15.
    Gong, N.Z., Xu, W., Huang, L., Mittal, P., Stefanov, E., Sekar, V., Song, D.: Evolution of social-attribute networks: measurements, modeling, and implications using Google+ . In: Proceedings of the 2012 Internet Measurement Conference (IMC ’12), pp. 131–144. ACM, New York (2012).
  16. 16.
    Hennig, M., Brandes, U., Pfeffer, J., Mergel, I.: Studying Social Networks: A Guide to Empirical Research. Campus Verlag, Frankfurt (2012)Google Scholar
  17. 17.
    Hmimida, M., Kanawati, R.: Community detection in multiplex networks: a seed-centric approach. Networks Heterog. Media 10(1), 71–85 (2015). MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Hollstein, B.: Qualitative approaches. In: Scott, J., Carrington, P.J. (eds.) The SAGE Handbook of Social Network Analysis, pp. 404–416. SAGE Publications, London (2014)CrossRefGoogle Scholar
  19. 19.
    Hric, D., Peixoto, T.P., Fortunato, S.: Network structure, metadata, and the prediction of missing nodes and annotations. Phys. Rev. X 6, 031038 (2016). Google Scholar
  20. 20.
    Hulovatyy, Y., Milenkovic, T.: Scout: simultaneous time segmentation and community detection in dynamic networks. Sci. Rep. 6, 37557 (2016)CrossRefGoogle Scholar
  21. 21.
    Hummon, N.P., Doreian, P., Freeman, L.C.: Analyzing the structure of the centrality-productivity literature created between 1948 and 1979. Knowledge 11(4), 459–480 (1990). CrossRefGoogle Scholar
  22. 22.
    Javed, M.A., Younis, M.S., Latif, S., Qadir, J., Baig, A.: Community detection in networks: a multidisciplinary review. J. Netw. Comput. Appl. 108, 87–111 (2018). CrossRefGoogle Scholar
  23. 23.
    Jeub, L.G.S., Mahoney, M.W., Mucha, P.J., Porter, M.A.: A local perspective on community structure in multilayer networks. Netw. Sci. 5(2), 144–163 (2017). CrossRefGoogle Scholar
  24. 24.
    Karney, C.F.F.: Algorithms for geodesics. J. Geodesy 87(1), 43–55 ( 2013). CrossRefGoogle Scholar
  25. 25.
    Kim, M., Leskovec, J.: Multiplicative attribute graph model of real-world networks. In: Kumar, R., Sivakumar, D. (eds.) Algorithms and Models for the Web-Graph, pp. 62–73. Springer, Berlin (2010)zbMATHCrossRefGoogle Scholar
  26. 26.
    Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014). CrossRefGoogle Scholar
  27. 27.
    Leifeld, P.: Discourse network analysis: policy debates as dynamic networks. In: The Oxford Handbook of Political Networks. Oxford University Press, Oxford (2017). Google Scholar
  28. 28.
    Li, J., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM ’17), pp. 387–396. ACM, New York (2017).
  29. 29.
    Masci, J., Bronstein, M.M., Bronstein, A.M., Schmidhuber, J.: Multimodal similarity-preserving hashing. IEEE Trans. Pattern Anal. Mach. Intell. 36(4), 824–830 (2014). CrossRefGoogle Scholar
  30. 30.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001). CrossRefGoogle Scholar
  31. 31.
    Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of the 17th International Conference on World Wide Web (WWW ’08), pp. 101–110. ACM, New York (2008).
  32. 32.
    Meng, L., Hulovatyy, Y., Striegel, A., Milenković, T.: On the interplay between individuals’ evolving interaction patterns and traits in dynamic multiplex social networks. IEEE Trans. Netw. Sci. Eng. 3(1), 32–43 (2016). CrossRefGoogle Scholar
  33. 33.
    Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003). MathSciNetCrossRefGoogle Scholar
  34. 34.
    Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S.: Continuous-time dynamic network embeddings. In: Companion Proceedings of the Web Conference 2018 (WWW ’18), pp. 969–976. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2018). CrossRefGoogle Scholar
  35. 35.
    Pelechrinis, K., Wei, D.: VA-index: Quantifying assortativity patterns in networks with multidimensional nodal attributes. PLoS One 11(1), 1–13 (2016). CrossRefGoogle Scholar
  36. 36.
    Pfeffer, J.: Visualization of political networks. In: Victor, J.N., Montgomery, A.H., Lubell, M. (eds.) The Oxford Handbook of Political Networks. Oxford University Press, Oxford (2017). Google Scholar
  37. 37.
    Rabbany, R., Eswaran, D., Dubrawski, A.W., Faloutsos, C.: Beyond assortativity: proclivity index for attributed networks (prone). In: Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X., Moon, Y.S. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 225–237. Springer, Cham (2017)CrossRefGoogle Scholar
  38. 38.
    Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdiscip. Rev. Comput. Stat. 7(3), 223–247 (2015). MathSciNetCrossRefGoogle Scholar
  39. 39.
    Schilit, W.N.: A system architecture for context-aware mobile computing. Ph.D. Thesis, Columbia University (1995)Google Scholar
  40. 40.
    Scott, J.: Social Network Analysis. Sage, Thousand Oaks (2017)Google Scholar
  41. 41.
    Sharma, R., Magnani, M., Montesi, D.: Investigating the types and effects of missing data in multilayer networks. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 392–399 (2015).
  42. 42.
    Snijders, T., Steglich, C., Schweinberger, M.: chap. Modeling the Coevolution of Networks and Behavior, pp. 41–71. Lawrence Erlbaum Associates Publishers, Mahwah (2007)CrossRefGoogle Scholar
  43. 43.
    Solá, L., Romance, M., Criado, R., Flores, J., García del Amo, A., Boccaletti, S.: Eigenvector centrality of nodes in multiplex networks. Chaos Interdiscip. J. Nonlinear Sci. 23(3), 033131 (2013). zbMATHCrossRefGoogle Scholar
  44. 44.
    Solé-Ribalta, A., De Domenico, M., Gómez, S., Arenas, A.: Centrality rankings in multiplex networks. In: Proceedings of the 2014 ACM Conference on Web Science (WebSci ’14), pp. 149–155. ACM, New York (2014).
  45. 45.
    Wang, J., Liu, W., Kumar, S., Chang, S.F.: Learning to hash for indexing big data—a survey. Proc. IEEE 104(1), 34–57 (2016). CrossRefGoogle Scholar
  46. 46.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)zbMATHCrossRefGoogle Scholar
  47. 47.
    Werner, M., Dorfmeister, F., Schönfeld, M.: Ambience: context-centric online social network. In: 12th Workshop on Positioning, Navigation and Communications (WPNC) (2015)Google Scholar

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Authors and Affiliations

  1. 1.Bavarian School of Public PolicyTechnical University in MunichMunichGermany

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