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Applications of Social Media and Social Network Analysis pp 103–124Cite as

Studying Graph Dynamics Through Intrinsic Time Based Diffusion Analysis

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Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Complex networks may be studied in various ways, e.g., by analyzing the evolutions of their topologies over time, and in particular of their community structures. In this paper, we focus on another type of dynamics, related to diffusion processes on these networks. Indeed, our work aims at characterizing network dynamics from the diffusion point of view, and reciprocally, it evaluates the impact of graph dynamics on diffusion. We propose in this paper an innovative approach based on the notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to somehow isolate the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on three real datasets and show the promising results of intrinsic time-based diffusion analysis.

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Notes

  1. 1.

    The choice of the transition considered as an intrinsic time unit will depend on the dataset, as explained in Sects. 4 and 5.

  2. 2.

    https://github.com/blog/1183-try-git-in-your-browser.

References

  1. Kermark M, Mckendrick A (1927) Contributions to the mathematical theory of epidemics. part i. Proc R Soc A 115(5):700–721

    Google Scholar 

  2. Jensen R (1982) Adoption and diffusion of an innovation of uncertain profitability. J Econ Theor 27(1):182–193

    Google Scholar 

  3. Barthelemy M, Barrat A, Pastor-Satorras R, Vespignani A (2005) Dynamical patterns of epidemic outbreaks in complex heterogeneous networks. J Theor Biol 235(2):275–288

    Google Scholar 

  4. Albano A, Guillaume J-L, Le Grand B (2012) File diffusion in a dynamic peer-to-peer network, in mining social network dynamics, in conjunction with the world wide web conference. ACM, pp 1169–1172

    Google Scholar 

  5. Fefferman N, Ng K (2007) How disease models in static networks can fail to approximate disease in dynamic networks. Phys Rev E 76(3):031919

    Article  MathSciNet  Google Scholar 

  6. Liu S-Y, Baronchelli A, Perra N (2013) Contagion dynamics in time-varying metapopulation networks. Phys Rev E 87(3):032805

    Google Scholar 

  7. Lee S, Rocha LE, Liljeros F, Holme P (2012) Exploiting temporal network structures of human interaction to effectively immunize populations. PLoS ONE 7(5):e36439

    Google Scholar 

  8. Delre SA, Jager W, Bijmolt TH, Janssen MA (2010) Will it spread or not? the effects of social influences and network topology on innovation diffusion. J Prod Innovation Manage 27(2):267–282

    Article  Google Scholar 

  9. Eames KT, Tilston NL, Brooks-Pollock E, Edmunds WJ (2012) Measured dynamic social contact patterns explain the spread of h1n1v influenza. PLoS Comput Biol 8(3):e1002425

    Google Scholar 

  10. Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102

    Article  Google Scholar 

  11. Albano A, Guillaume J-L, Heymann S, Le Grand B (2013) A matter of time—intrinsic or extrinsic—for diffusion in evolving complex networks. In: Proceedings of the international conference on advances in social networks analysis and mining (ASONAM)

    Google Scholar 

  12. Heymann S, Grand BL (2013) Monitoring user-system interactions through graph-based intrinsic dynamics analysis. In: Proceedings of the 7th IEEE international conference on research challenges in information science

    Google Scholar 

  13. Newton I (1687) Philosophiæ Naturalis Principia Mathematica

    Google Scholar 

  14. Kant I (1781) Kritik der reinen Vernunft

    Google Scholar 

  15. de la Convention du Mètre OI (2006) The international system of units (SI), Bureau international des Poids et Mesures. Tech Rep 8

    Google Scholar 

  16. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Google Scholar 

  17. Github press page [Online]. https://github.com/about/press

  18. Github archive [Online]. http://www.githubarchive.org

  19. Isella L, Stehlé J, Barrat A, Cattuto C, Pinton J-F, Van den Broeck W (2011) What’s in a crowd? analysis of face-to-face behavioral networks. J Theor Biol 271(1):166–180

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the French National Research Agency contract DynGraph ANR-10-JCJC-0202, and by the CODDDE project ANR-13-CORD-0017-01.

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Authors and Affiliations

  1. Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France

    Alice Albano, Jean-Loup Guillaume, Sébastien Heymann & Bénédicte Le Grand

  2. CNRS, UMR 7606, LIP6, F-75005, Paris, France

    Alice Albano, Jean-Loup Guillaume, Sébastien Heymann & Bénédicte Le Grand

  3. Université Paris 1 Panthéon-Sorbonne, CRI, France

    Alice Albano, Jean-Loup Guillaume, Sébastien Heymann & Bénédicte Le Grand

Authors
  1. Alice Albano
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  2. Jean-Loup Guillaume
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  3. Sébastien Heymann
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  4. Bénédicte Le Grand
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Corresponding author

Correspondence to Bénédicte Le Grand .

Editor information

Editors and Affiliations

  1. Department of Computational Intelligence, Wroclaw University of Technology, Wroclaw, Poland

    Przemysław Kazienko

  2. Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, Indiana, USA

    Nitesh Chawla

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Albano, A., Guillaume, JL., Heymann, S., Grand, B.L. (2015). Studying Graph Dynamics Through Intrinsic Time Based Diffusion Analysis. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-19003-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19002-0

  • Online ISBN: 978-3-319-19003-7

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