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A Survey of Community Detection Algorithms Based On Analysis-Intent

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Cyber Warfare

Part of the book series: Advances in Information Security ((ADIS,volume 56))

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Abstract

There has been a significant amount of research dedicated to identifying community structures within graphs. Most of these studies have focused on partitioning techniques and the resultant quality of discovered groupings (communities) without regard for the intent of the analysis being conducted (analysis-intent). In many cases, a given network community can be composed of significantly different elements depending upon the context in which a partitioning technique is used or applied. Moreover, the number of communities within a network will vary greatly depending on the analysis-intent and thus the discretion quality and performance of algorithms will similarly vary. In this survey we review several algorithms from the literature developed to discover community structure within networks. We review these approaches from two analysis perspectives: role/process focused (category-based methods) and topological structure or connection focused (event-based methods). We discuss the strengths and weaknesses of each algorithm and provide suggestions on the algorithms’ use depending on analysis context.

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Notes

  1. 1.

    In the botnet scenario depicted in Fig.12.1 we only consider three layers of nodes (Botmaster, Bots, and Victims). In many cases a fourth layer is present in between the Botmaster and the Bots (Command and Control). We chose not to include this layer for sake of clarity. Also, in many cases the Botmaster layer and the Command and Control layer are the same.

References

  • Block, P., Community: The Structure of Belonging, Berrett-Koehler, 2008.

    Google Scholar 

  • Laut, I., Rath, C., Worner, L., Nosenko, V., Zhdanov, S., Schablinski, J., Block, D., Thomas, H., and Morfill, G., Network analysis of three-dimensional complex plasma clusters in a rotating electric field. Physical Review E, 2014.89(2).

    Google Scholar 

  • Kim, E, Hwang, D, and Ko, T., Multiscale ensemble clustering for finding modules in complex networks. Physical Review E, 2012.85(2): p. 026119.

    Google Scholar 

  • Scibetta, M., Boano, f., Revelli, R., and Ridolfi, L., Community Detection as a Tool for District Metered Areas Identification. Procedia Engineering, 2014.70: p. 1518.

    Google Scholar 

  • Fortunato, S.,Community detection in graphs. Physics Reports, 2010.486: p. 99.

    Google Scholar 

  • Jin, D., et al.,Extending a configuration model to find communities in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2013.2013(09): p. P09013.

    Article  Google Scholar 

  • Jia, Y., Garland, M., and Hart, J., Social Network Clustering and Visualization using Hierarchical Edge Bundles. Computer Graphics Forum, 2011: p. no.

    Google Scholar 

  • Leydesdorff, L. and Ahrweiler, P., In search of a network theory of innovations: Relations, positions, and perspectives. Journal of the Association for Information Science and Technology, 2014: p. n/a.

    Google Scholar 

  • Evans, T.S.,Clique graphs and overlapping communities. Journal of Statistical Mechanics: Theory and Experiment, 2010.2010(12): p. P12037.

    Article  Google Scholar 

  • Community detection algorithms: A comparative analysis. Physical Review E, 2009.80(5): p. 056117.

    Google Scholar 

  • Branting, K.L., Context-Sensitive Detection of Local Community Structure, Social Network Analysis and Mining, 1869–5450:1–11, Springer (2011)

    Google Scholar 

  • Palla, G., et al., Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys (CSUR), 2005.435(7043): p. 5.

    Google Scholar 

  • Dietrich, C.J., C. Rossow, and N. Pohlmann, CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis. Computer Networks, 2013.57(2): p. 475–486.

    Article  Google Scholar 

  • Chan, S.-Y., P. Hui, and K. Xu. CommunityDetection of Time-Varying Mobile Social Networks. in1st International Conference on Complex Sciences: Theory and Applications (Complex 2009). 2009. Shanghai, China: Springer.

    Google Scholar 

  • Jiashun, J.FAST COMMUNITY DETECTION BY SCORE. 2012; Available from: arXiv:1211.5803.

    Google Scholar 

  • Dang, T.A. and E. Viennet.Community Detection based on Structural and Attribute Similarities. inThe Sixth International Conference on Digital Society. 2012. Valencia, Spain: IARIA.

    Google Scholar 

  • Girvan, M. and M.E.J. Newman,Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 2002.99(12): p. 7821–7826.

    Google Scholar 

  • Newman, M.E.J. and M. Girvan,Finding and evaluating community structure in networks. Physics Review E, 2004.69(026113).

    Google Scholar 

  • Freeman, L.C., A Set of Measures of Centrality Based on Betweenness. Sociometry, 1977.40(1): p. 7.

    Article  MathSciNet  Google Scholar 

  • Yoo, A., et al.A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L. inACM/IEEE Conference on Supercomputing. 2005. Washington, D.C.

    Google Scholar 

  • Ward, J.H.,Hierarchical grouping to optimize an objective function. Journal of the American Stastical Association, 1963.58.

    Google Scholar 

  • Orman, G.K., V. Labatut, and H. Cherifi,Comparative evaluation of community detection algorithms: a topological approach. Journal of Statistical Mechanics: Theory and Experiment, -2012.2012(08): p. P08001.

    Article  Google Scholar 

  • Huang, J., Sun, H., Han, J., Feng, B.,Density-based shrinkage for revealing hierarchical and overlapping community structure in networks. Physica A Statistical Mechanics and its Applications, 2011.390(11): p. 2160.

    Google Scholar 

  • MacKay, D.J.C.,Information Theory, Inference, and Learning Algorithms, ed. C.U. Press. Vol. 1. 1995, Cambridge: Cambridge Univeristy Press. 640.

    Google Scholar 

  • Morup, M. and M.N. Schmidt,Bayesian community detection. Neural Computation, 2012.

    Google Scholar 

  • Karrer, B. and M.E.J. Newman,Stochastic blockmodels and community structure in networks. Physics Review E, 2011.83(016107): p. 11.

    MathSciNet  Google Scholar 

  • Hierarchical Block Structures and High-Resolution Model Selection in Large Networks. Physical Review X, 2014.4(1).

    Google Scholar 

  • Nascimento, M. and Pitsoulis, L.,Community detection by modularity maximization using GRASP with path relinking. Computers & Operations Research, 2013.

    Google Scholar 

  • Tyler, J.R., D.M. Wilkinson, and B.A. Huberman.Email as spectorscopy: automated discovery of community structure within organizations. inInternational Conference on Communities and Technologies. 2003. Kluwer Academic, Dordrecht.

    Google Scholar 

  • Sales-Pardo, M., et al.,Extracting the hierarchical organization of complex systems. National Academy of Sciences, 2007.104(39).

    Google Scholar 

  • Clauset, A., C. Moore, and M.E.J. Newman,Hierarchical structure and the prediction of missing links in networks. 453, 2008.7191(98).

    Google Scholar 

  • Liben-Nowell, D. and J. Kleinberg.The Link-Prediction Problem for Social Networks. in Twelth International Conference on Information and Knowledge Management. 2003. New York, NY: ACM.

    Google Scholar 

  • Clauset, A., M.E.J. Newman, and C. Moore,Finding community structure in very large networks. Physical Review E, 2004.70(066111).

    Google Scholar 

  • Hopcroft, J., et al.,Natural Communities in Large Linked Networks. Proceedings of the National Academy of Sciences, 2004.101(5249).

    Google Scholar 

  • Bagrow, J.P. and E.M. Bollt,A local method for detecting communties. Physical Review E, 2005.72(046108).

    Google Scholar 

  • Rodrigues, F.A., G. Travieso, and L.d.F. Costa,Characterization of complex networks: A survey of measurements. International Journal of Modern Physics C, 2007.18(937).

    Google Scholar 

  • Ahn, Y., J.P. Bagrow, and S. Lehmann.Communities and Hierarchical Organization of Links in Complex Networks. 2009; Available from: arxiv:0903.3178.

    Google Scholar 

  • Wang, J., et al.,A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011.8(3).

    Google Scholar 

  • Shen, H.-W., X.-Q. Cheng, and J.-F. Guo,Quantifying and identifying the overlapping community structure in networks. Journal of Statistical Mechanics: Theory and Experiment, 2009.2009(07): p. P07042.

    Article  Google Scholar 

  • Nicosia, V., et al.,Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech., 2009(P03024).

    Google Scholar 

  • Papadopoulos, S., et al.Bridge Bounding: A Local Approach for Efficient Community Discovery in Complex Networks. 2009; Available from: arXiv:0902.0871.

    Google Scholar 

  • Reichardt, J. and S. Bornholdt.Stastical Mechanics of Community Detection. 2006; Available from: arXiv:cond-mat/0603718.

    Google Scholar 

  • Barber, M.J., et al.,Searching for communities in bipartie networks. In Proceedings of 5th Jagna International Workshop on Stochastic and Quantum Dynamics of Biomolecular Systems, 2008.

    Google Scholar 

  • Danon, L., et al.,Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005.2005(September 2005).

    Google Scholar 

  • Guimera, R., et al.,Community analysis in social networks. Physics Review E, 2003.68(065103).

    Google Scholar 

  • Li, J.Z., et al., Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation. Science, 2008.319(5866).

    Google Scholar 

  • Fortunato, S. and M. Barthelemy,Resolution limit in community detection. Proceedings of the National Academy of Sciences, 2006.104(1).

    Google Scholar 

  • Ruan, J. and W. Zhang,Identifying network communities with a high resolution. Physics Review E, 2008.77(016104).

    Google Scholar 

  • Berry, J.W., et al.Tolerating the Community Detection Resolution Limit with Edge Weighting. Physics and Society 2009; Available from: arxiv.org/abs/0903.1072.

    Google Scholar 

  • Hofman, J. and C. Wiggins,Bayesian approach to network modularity. Physical review letters, 2008.100(258701).

    Google Scholar 

  • Psorakis, I., Roberts,S., Ebden, M., and Sheldon, B., Overlapping Community Detection Using Bayesian Non-Negative Matrix Factorization, Phys., Rev. E83, June 2011

    Google Scholar 

  • Pisorakis, I., Roberts, S., Ebden, M., and Sheldon, B.,Overlapping community detection using Bayesian non-negative matrix factorization. Physical Review E, 2011.83(6): p. 066114.

    Google Scholar 

  • Airoldi, E., et al.,Mixed membership stochastic blockmodels. Journal of Machine Learning Research, 2008.9.

    Google Scholar 

  • Barbieri, N., F. Bonchi, and G. Manco.Cascade-based Community Detection. inProceedings of the sixth ACM International Conference on Web Search and Data Mining. 2013. New York, NY: ACM.

    Google Scholar 

  • Chen, Y., V. Kawadia, and R. Urgaonkar.Detecting Overlapping Temporal Community Structure in Time-Evolving Networks. 2013; Available from: arXiv:1303.7226.

    Google Scholar 

  • Rosvall, M. and C.T. Bergstrom,An information-theoretic framework for resolving community structure in complex networks. Proceedings of the National Academy of Sciences, 2007.104(18): p. 7327–7331.

    Article  Google Scholar 

  • Rosvall, M. and C. Bergstrom,Maps of random walks on complex networks reveal community structure. National Academy of Sciences, 2008.105(4).

    Google Scholar 

  • Rosvall, M., D. Axelsson, and C.T. Bergstrom,The map equation. The European Physical Journal Special Topics, 2009.178.

    Google Scholar 

  • Lehmann, S., M. Schwartz, and L.K. Hansen,Biclique communities. Physical Review E, 2008.78(016108).

    Google Scholar 

  • Kumpula, J.M., et al.,Sequential algorithm for fast clique percolation. Physical Review E, 2008.78(026109).

    Google Scholar 

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Authors and Affiliations

  1. Information Technology Division, Naval Research Laboratory, 4555 Overlook Ave., 20375, Washington, DC, USA

    Napoleon C. Paxton, Stephen Russell, Ira S. Moskowitz & Paul Hyden

Authors
  1. Napoleon C. Paxton
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  2. Stephen Russell
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  3. Ira S. Moskowitz
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  4. Paul Hyden
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Corresponding author

Correspondence to Napoleon C. Paxton .

Editor information

Editors and Affiliations

  1. Center for Secure Information Systems, George Mason University, Fairfax, Virginia, USA

    Sushil Jajodia

  2. Arizona State University, Tempe, Arizona, USA

    Paulo Shakarian

  3. Computer Science Department, University of Maryland, College Park, Maryland, USA

    V.S. Subrahmanian

  4. The MITRE Corporation, McLean, Virginia, USA

    Vipin Swarup

  5. Information Sciences Directorate, Triangle Park, North Carolina, USA

    Cliff Wang

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Paxton, N., Russell, S., Moskowitz, I., Hyden, P. (2015). A Survey of Community Detection Algorithms Based On Analysis-Intent. In: Jajodia, S., Shakarian, P., Subrahmanian, V., Swarup, V., Wang, C. (eds) Cyber Warfare. Advances in Information Security, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-14039-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-14039-1_12

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