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Dynamic Social Network Analysis Using Author-Topic Model

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Innovations for Community Services (I4CS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 863))

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Abstract

In this paper, we proposed an agent-based model for analyzing dynamic social network associated to textual information using author-topic model, namely Textual-ABM. Author-topic model is chosen to estimate topic’s distribution transformation of agents in the agent-based model since it models the content of documents and also interests of authors. Textual-ABM can be utilized to discover dynamic of a social network which includes not only network structure but also node’s properties over time. Furthermore, we introduced independent cascade model based on homophily, namely H-IC. The infected probability associated with each edge is homophily or similarity which measured based on topic’s distribution. We have applied our methodology to a collected data set from NIPS and have obtained satisfactory results.

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Notes

  1. 1.

    https://pypi.python.org/pypi/gensim.

  2. 2.

    https://www.theguardian.com/international.

  3. 3.

    https://www.theguardian.com/us-news/blog/2016/aug/25/resentful-americans-turn-blind-eye-donald-trump.

References

  1. Breiger, R.L., Carley, K.M., Pattison, P.: Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers. National Academies Press, Washington, D.C. (2003)

    Google Scholar 

  2. Buntine, W.: Estimating likelihoods for topic models. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS (LNAI), vol. 5828, pp. 51–64. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05224-8_6

    Chapter  Google Scholar 

  3. Carley, K.M., Martin, M.K., Hirshman, B.R.: The etiology of social change. Topics Cogn. Sci. 1(4), 621–650 (2009)

    Article  Google Scholar 

  4. Gayraud, N.T., Pitoura, E., Tsaparas, P.: Diffusion maximization in evolving social networks. In: Conference on Online Social Networks, pp. 125–135 (2015)

    Google Scholar 

  5. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. In: Proceedings of the National Academy of Sciences of the United States of America, pp. 7821–7826, June 2002

    Article  MathSciNet  Google Scholar 

  6. Grandjean, M.: A social network analysis of Twitter: mapping the digital humanities community. Cogent Arts Humanit. 3(1), 1171458 (2016)

    Article  Google Scholar 

  7. Hoffman, M., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. arXiv:1206.7051 [cs, stat], June 2012

  8. Holme, P., Saramki, J.: Temporal networks. Phys. Rep. 519, 97–125 (2012)

    Article  Google Scholar 

  9. Legendi, R.O., Gulys, L.: Agent-based dynamic network models: validation on empirical data. In: Advances in Social Simulation, pp. 49–60 (2014)

    Google Scholar 

  10. Blei, D.M., Ng, A.Y., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 601–608 (2001)

    MATH  Google Scholar 

  11. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)

    Article  Google Scholar 

  12. Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)

    Article  Google Scholar 

  13. Otte, E., Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28(6), 441–453 (2002)

    Article  Google Scholar 

  14. Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. arXiv:1207.4169 [cs, stat], July 2012

  15. Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R.: Diffusion in Social Networks. Springer, New York (2015). Google-Books-ID: zouQCgAAQBAJ

    Book  Google Scholar 

  16. Zhuang, H., Sun, Y., Tang, J., Zhang, J., Sun, X.: Influence maximization in dynamic social networks. In: International Conference on Data Mining, pp. 1313–1318, December 2013

    Google Scholar 

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Author information

Authors and Affiliations

  1. CHArt Laboratory EA 4004, EPHE, PSL Research University, Paris, France

    Kim Thoa Ho, Quang Vu Bui & Marc Bui

  2. University of Education, Hue University, Hue, Vietnam

    Kim Thoa Ho

  3. University of Sciences, Hue University, Hue, Vietnam

    Quang Vu Bui

Authors
  1. Kim Thoa Ho
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  2. Quang Vu Bui
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  3. Marc Bui
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Corresponding author

Correspondence to Kim Thoa Ho .

Editor information

Editors and Affiliations

  1. University of Žilina, Žilina, Slovakia

    Michal Hodoň

  2. Telekom Innovation Laboratories, Deutsche Telekom AG, Darmstadt, Hessen, Germany

    Gerald Eichler

  3. EAH Jena, Jena, Germany

    Christian Erfurth

  4. University of Hagen, Hagen, Germany

    Günter Fahrnberger

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Ho, K.T., Bui, Q.V., Bui, M. (2018). Dynamic Social Network Analysis Using Author-Topic Model. In: Hodoň, M., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2018. Communications in Computer and Information Science, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-319-93408-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-93408-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93407-5

  • Online ISBN: 978-3-319-93408-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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