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Multi-layer Network Composition Under a Unified Dynamical Process

  • Xiaoran YanEmail author
  • Shang-Hua Teng
  • Kristina Lerman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

In this paper, we take a step towards a principled method of network composition from multi-layer data. We argue that inter-layer dynamics is a essential component of understanding the structure as a whole. Mathematically, we consider the following abstract problem: given multiple layers of network data over a shared vertex set, and additional parameters for inter-layer transitions, construct a (single) weighted network that best integrates the multi-layer dynamics. In this context, we will also study an empirical use case of the composition framework.

References

  1. 1.
    Acar, E., Yener, B.: Unsupervised multiway data analysis: a literature survey. IEEE Trans. Knowl. Data Eng. 21(1), 6–20 (2009)CrossRefGoogle Scholar
  2. 2.
    Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J.J., Vespignani, A.: Multiscale mobility networks and the spatial spreading of infectious diseases. Proc. Natl. Acad. Sci. 106(51), 21484–21489 (2009)CrossRefGoogle Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 10, 10008 (2008)CrossRefGoogle Scholar
  4. 4.
    Borgatti, S.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)MathSciNetCrossRefGoogle Scholar
  5. 5.
    De Domenico, M., Sole, A., Gomez, S., Arenas, A.: Random walks on multiplex networks. ArXiv e-prints, June 2013Google Scholar
  6. 6.
    Dunning, D., Cohen, G.L.: Egocentric definitions of traits and abilities in social judgment. J. Personal. Soc. Psychol. 63(3), 341–355 (1992)CrossRefGoogle Scholar
  7. 7.
    Gallotti, R., Barthelemy, M.: The multilayer temporal network of public transport in Great Britain. Sci. Data 2, 140056 (2015)CrossRefGoogle Scholar
  8. 8.
    Ghosh, R., Teng, S.H., Lerman, K., Yan, X.: The interplay between dynamics and networks: centrality, communities, and cheeger inequality. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1406–1415. KDD 2014, ACM, New York (2014)Google Scholar
  9. 9.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003, pp. 137–146. ACM (2003)Google Scholar
  10. 10.
    Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. ArXiv e-prints, September 2013Google Scholar
  11. 11.
    Lambiotte, R., Delvenne, J.C., Barahona, M.: Laplacian dynamics and multiscale modular structure in networks. arXiv preprint arXiv:0812.1770 (2008)
  12. 12.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web (1999)Google Scholar
  15. 15.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Indiana University Network Science InstituteIndiana UniversityBloomingtonUSA
  2. 2.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Information Sciences InstituteUniversity of Southern CaliforniaLos AngelesUSA

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