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Identification of Neutral Members in Social Networks using a Distance and Fuzzy based Model

by A. Galappaththi, M. A. P. Chamikara, Y.p.r. D. Yapa, S. R. Kodituwakku, J. Gunatilake
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 22
Year of Publication: 2015
Authors: A. Galappaththi, M. A. P. Chamikara, Y.p.r. D. Yapa, S. R. Kodituwakku, J. Gunatilake
10.5120/21365-4389

A. Galappaththi, M. A. P. Chamikara, Y.p.r. D. Yapa, S. R. Kodituwakku, J. Gunatilake . Identification of Neutral Members in Social Networks using a Distance and Fuzzy based Model. International Journal of Computer Applications. 119, 22 ( June 2015), 1-5. DOI=10.5120/21365-4389

@article{ 10.5120/21365-4389,
author = { A. Galappaththi, M. A. P. Chamikara, Y.p.r. D. Yapa, S. R. Kodituwakku, J. Gunatilake },
title = { Identification of Neutral Members in Social Networks using a Distance and Fuzzy based Model },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 22 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number22/21365-4389/ },
doi = { 10.5120/21365-4389 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:01.709610+05:30
%A A. Galappaththi
%A M. A. P. Chamikara
%A Y.p.r. D. Yapa
%A S. R. Kodituwakku
%A J. Gunatilake
%T Identification of Neutral Members in Social Networks using a Distance and Fuzzy based Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 22
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Understanding community structure helps to interpret the role of actors in a social network. Actor has close ties to actors within a community than actors outside of its community. Community structure reveals important information such as central members in communities and bridges members who connect communities. Clustering algorithms like hierarchical clustering, affinity propagation, modularity and spectral graph clustering had been applied in social network clustering to identify community structures in it. This study proposes a novel method for distance measurement between nodes and centroids. Distance is measured based on the shortest path length and number of common nearest neighbors with one path length. This measure, "Proportional closeness" is used to assign nodes to the closest centroid. A fuzzy system is also applied to find the closest centroid to a node when similar proportional closeness values are present for multiple centroids. The method has been applied to two artificial networks and one real world network data to test its accuracy on membership identification. The results revealed that the method successfully assigns members to its nearest centroid and leave neutral members aside without assigning to any centroid.

References
  1. Hanneman, R. A. , & Riddle, M. 2005. Introduction to social network methods.
  2. Newman, M. E. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577-8582.
  3. White, H. C. , Boorman, S. A. , & Breiger, R. L. 1976. Social structure from multiple networks. I. Blockmodels of roles and positions. American journal of sociology, 730-780.
  4. Xu, J. J. , & Chen, H. 2005. CrimeNet explorer: a framework for criminal network knowledge discovery. ACM Transactions on Information Systems (TOIS), 23(2), 201-226.
  5. John Scott, & Peter J. Carrington (Eds. ). (2011). The SAGE handbook of social network analysis. SAGE publications.
  6. Frey, B. J. , & Dueck, D. 2007. Clustering by passing messages between data points. Science, 315(5814), 972-976.
  7. Fiedler, M. 1973. Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23(2), 298-305.
  8. Girvan, M. , & Newman, M. E. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821-7826.
  9. Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2), 321-330.
  10. Zachary, W. W. 1977. An information flow model for conflict and fission in small groups. Journal of anthropological research, 452-473.
  11. MATLAB version R. 2011aFuzzy logic toolbox version Natick, Massachusetts: The MathWorks Inc. , 2010.
  12. S. N. Sivanandam, S. Sumathi and S. N. Deepa. Introduction to Fuzzy Logic using MATLAB, 2007, 120-131
  13. Gergana. 2014. Octave Networks Toolbox First Release. ZENODO. http://doi. org/10. 5281/zenodo. 10778
  14. John W. Eaton, David Bateman, S?©renHauberg, RikWehbring 2014. GNU Octave version 3. 8. 1 manual: a high-level interactive language for numerical computations. CreateSpace Independent Publishing Platform. ISBN 1441413006,URL http://www. gnu. org/software/octave/doc/interpreter/
Index Terms

Computer Science
Information Sciences

Keywords

Eigenvector centrality centroids fuzzy system proportional closeness fuzzy closeness

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