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A Connectionist Model-Based Approach to Centrality Discovery in Social Networks

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Behavior and Social Computing (BSIC 2013, BSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8178))

Abstract

Identifying key nodes in networks, in terms of centrality measurement, is one of the popular research topics in network analysis. Various methods have been proposed with different interpretations of centrality. This paper proposes a novel connectionist method which measures node centrality for directed and weighted networks. The method employs a spreading activation mechanism in order to measure the influence of a given node on the others, within an information diffusion circumstance. The experimental results show that, compared with other popular centrality measurement methods, the proposed method performs the best for finding the most influential nodes.

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References

  1. Landherr, A., Friedl, B., Heidemann, J.: A Critical Review of Centraility Measures in Social Networks. Bussiness and Information Systems Engineering 2(6), 371–385 (2010)

    Article  Google Scholar 

  2. Zhang, Y., Zhang, Z., Wei, D., Deng, Y.: Centrality Measure in Weighted Networks Based on an Amoeboid Algorithm. Journal of Information and Computational Science 9(2), 369–376 (2012)

    Google Scholar 

  3. Yan, X., Zhai, L., Fan, W.: C-Index: A Weighted Network Node Centrality Measure for Collaboration Competence. Journal of Informetrics 7(1), 223–239 (2013)

    Article  Google Scholar 

  4. Qi, X., Fuller, E., Wu, Q., Wu, Y., Zhang, C.Q.: Laplacian Centrality: A New Centrality Measure for Weighted Networks. Information Sciences 194, 240–253 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  5. Kiss, C., Bichler, M.: Identification of influencers - measuring influence in customer networks. Decis. Support Syst. 46(1), 233–253 (2008)

    Article  Google Scholar 

  6. Borgatti, S.P.: Identifying Sets of Key Players in A Social Network. Comput. Math. Organ. Theory 12(1), 21–34 (2006)

    Article  MATH  Google Scholar 

  7. Borgatti, S.P.: Centrality and Network Flow. Social Networks 27(1), 55–71 (2005)

    Article  Google Scholar 

  8. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the Spread of Influence Through a Social Network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146 (2003)

    Google Scholar 

  9. Mcclelland, J.L.: Connectionist Models and Bayesian Inference. Oxford University Press (1999)

    Google Scholar 

  10. Thagard, P., Litt, A.: Models of scientific explanation. In: The Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press (2006)

    Google Scholar 

  11. Glöckner, A., Betsch, T.: Modelling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberate decision making. Judgement and Decision Making 3, 215–228 (2008)

    Google Scholar 

  12. Betsch, T., Glöckner, A.: Intuition in judgment and decision making: Extensive thinking without effort. Psychological Inquiry 21(4), 279–294 (2010)

    Article  Google Scholar 

  13. Ratcliff, R., Zandt, T.V.: Connectionist and Diffusion Models of Reaction Time. Psychological Review 106(2), 261–300 (1999)

    Article  Google Scholar 

  14. Mcclelland, J.L., Rumelhart, D.E.: An Interactive Activation Model of Context Effects in Letter Perception: I. An Account of Basic Findings. Psychological Review 88, 375–407 (1981)

    Article  Google Scholar 

  15. Read, S.J., Vanman, E.J., Miller, L.: Connectionism, Parallel Constraint Satisfaction Process, and Gestalt Principles (Re) Introducing Cognitive Dynamics to Social Psychology. Personality and Social Psychology Review 1(1), 26–53 (1997)

    Article  Google Scholar 

  16. Thagard, P.: Coherence as constraint satisfaction. Cognitive Science 22(1), 1–24 (1998)

    Article  MathSciNet  Google Scholar 

  17. Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 163–177 (2001)

    Article  MATH  Google Scholar 

  18. Sheskin, D.: Handbook of Parametric and Nonparametric Statistical Procedures, 3rd edn. Taylor & Francis (2003)

    Google Scholar 

  19. Brin, S., Page, L.: The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)

    Article  Google Scholar 

  20. Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research 33, 452–473 (1977)

    Google Scholar 

  21. Bernard, H.R., Killworth, P.D., Sailer, L.: Informant Accuracy in Social Network Data IV: a Comparison of Clique-level Structure in Behavioral and Cognitive Network Data. Social Networks 2(3), 191–218 (1979)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. RMIT University, Australia

    Qingmai Wang, Xinghuo Yu & Xiuzhen Zhang

Authors
  1. Qingmai Wang
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  2. Xinghuo Yu
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  3. Xiuzhen Zhang
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Editor information

Editors and Affiliations

  1. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, NSW 2008, Sydney, Australia

    Longbing Cao

  2. Institute of Scientific and Industrial Research, Osaka University, Japan

    Hiroshi Motoda

  3. Department of Computer Science, University of Minnesota, USA

    Jaideep Srivastava

  4. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim

  5. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Rm 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

  7. Leibniz Universität Hannover, Germany

    Wolfgang Nejdl

  8. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

  9. Deakin University, Burwood, VIC, Australia

    Gang Li

  10. Shanghai Jiao Tong University, 200240, Shanghai, China

    Ya Zhang

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© 2013 Springer International Publishing Switzerland

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Cite this paper

Wang, Q., Yu, X., Zhang, X. (2013). A Connectionist Model-Based Approach to Centrality Discovery in Social Networks. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-04048-6_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04047-9

  • Online ISBN: 978-3-319-04048-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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