Abstract
Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear online at www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstract command for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstract command if the abstract is also to appear in the printed version of the book.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
B. Krishnamurthy, An improved min-cut algorithm for partitioning VLSI networks, IEEE Trans. Comp., Vol. 33, No. 5, 1984, pp. 438–446
L. Hagen, and A.B. Kahng, New spectral methods for ratio cut partition and clustering, IEEE Trans. Computer-Aided Design, Vol. 11, No. 9, 1992, pp. 1074–1085
M.T. Heath, E.G.Y. Ng, and B.W. Peyton, Parallel algorithm for sparse linear systems, SIAM Rev., Vol. 33, 1991, pp. 420–460
Pothen, H. Simon, and K.P. Liou, Partitioning sparse matrices with eigenvalues of graphs, SIAM J. Matrix Anal. Appl., Vol. 11, No. 3, 1990, pp. 430–452
H. Simon, Partitioning of unstructured problems for parallel processing, Comput. Syst. Eng., Vol. 2, No. 3, 1991, pp. 135–148
B. Hendrickson, and R. Leland, An improved spectral graph partitioning algorithm for mapping parallel computations, SIAM J. Comp. Sci., Vol. 16, No. 2, 1995, pp. 452–469
J. Shi, and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22, 2000, pp. 888–904
M. Girvan and M.E.J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci., Vol. 9, 2002, pp. 7821–7826
M.E.J. Newman and M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E, Vol. 69, 2004, 026113-026115
G. Palla, A.L. Barabasi, T. Vicsek, Quantifying social group evolution, Nature, Vol. 446, No. 7136, 2007, pp. 664–667
V. Spirin, L.A. Mirny, Protein complexes and functional modules in molecular networks, Proc. Natl. Acad. Sci., Vol. 100, No. 21, 2003, p. 12123
D.M. Wilkinson, B.A. Huberman, A method for finding communities of related genes, Proc. Natl. Acad. Sci., Vol. 101, 2004, pp. 5241–5248
R. Guimera, and L.A.N. Amaral, Functional cartography of complex metabolic networks, Nature, Vol. 433, 2005, pp. 895–900
J.M. Kleinberg, Authoritative sources in a hyperlinked environment, J. ACM, Vol. 46, No. 5, 1999, pp. 604–632
G.W. Flake, S. Lawrence, C.L. Giles, F.M. Coetzee, Self-organization and identification of Web communities, IEEE Comp., Vol. 35, No. 3, 2002, pp. 66–71
H. Ino, M. Kudo, A. Nakamura, Partitioning of Web graphs by community topology, Proc. of the 14th International Conference on World Wide Web (WWW’05), 2005, pp. 661–669
M. Fiedler, Algebraic connectivity of graphs, Czechoslovakian Math. J., Vol. 23, 1973, pp. 298–305
M. Fiedler, A Property of eigenvectors of nonnegative symmetric matrices and its application to graph theory, Czechoslovakian Math. J., Vol. 25, 1975, pp. 619–637
A. Pothen, H. Simon, and K.P. Liou, Partitioning sparse matrices with eigenvectors of graphs, SIAM J. Matrix Anal. Appl., Vol. 11, 1990, pp. 430–452
Y.C. Wei and C.K. Cheng, Ration cut partitioning for hierarchical designs, IEEE Trans. Computer-Aided Design, Vol. 10, No. 7, 1991, pp. 911–921
B.W. Kernighan, and S. Lin, An efficient heuristic procedure for partitioning graphs, Bell System Technical, Vol. 49, 1970, pp. 291–307
M.E.J. Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, Vol. 69, 2004, pp. 066133
Z. Wang, and J. Zhang, In search of the biological significance of modular structures in protein networks, PLOS Comp. Bio., Vol. 3, No. 6, 2007, p. e107
J.M. Pujol, J. Bejar, and J. Delgado, Clustering algorithm for determining community structure in large networks, Phys. Rev. E, Vol. 74, 2006, p. 016107
M.E.J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci., Vol. 103, No. 23, 2006, pp. 8577–8582
J. Reichardt and S. Bornholdt, Detecting fuzzy community structures in complex networks with a potts model, Phys. Rev. Let., Vol. 93, No. 19, 2004, p. 218701
F. Wu and B.A. Huberman, Finding communities in linear time: a physics approach, Euro. Phys. J. B, Vol. 38, 2004, pp. 331–338
G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structures of complex networks in nature and society, Nature, Vol. 435, No. 7043, 2005, pp. 814–818
B. Yang, W.K. Cheung, and J. Liu, Community mining from signed social networks, IEEE Trans. Knowledge and Data Eng., Vol. 19, No. 10, 2007, pp. 1333–1348
M.R. Garey, and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, CA, 1979
B. Yang, D. Liu, J. Liu, D. Jin, and H. Ma, Complex network clustering algorithms, J. Software, Vol. 20, No. 1, 2008, pp. 54–66
R. Guimera, M. Sales and L.A.N. Amaral, Modularity from fluctuations in random graphs and complex networks, Phys. Rev. E, Vol. 70, 2004, 025101
S. Fortunato, M. Barthelemy, Resolution limit in community detection, Proc. of the National Academy of Science, Vol. 104, No. 1, 2007, pp. 36–41
J. Reichardt and S. Bornholdt, Detecting fuzzy community structures in complex networks with a potts model, Phys. Rev. Let., Vol. 93, No. 19, 2004, p. 218701
F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, Defining and Identifying communities in networks, Proc. Natl. Acad. Sci., Vol. 101, No. 9, 2004, pp. 2658–2663
J.R. Tyler, D.M. Wilkinson, and B.A. Huberman, Email as spectroscopy: automated discovery of community structure within organizations, Proc. of the 1st International Conference on Communities and Technologies, 2003
W.Y. Chen, D. Zhang, E.Y. Chang, Combinational collaborative filtering for personalized community recommendation, Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Ming (KDD’08), 2008
A.V. Goldberg and R.E. Tarjan, A new approach to the maximum flow problem, J. ACM, Vol. 35, No. 4, 1988, pp. 921–940
A.V. Goldberg, Recent developments in maximum flow algorithms, Proc. of the 6th Scandinavian Workshop on Algorithm Theory, 1998
S. Wasserman and K. Faust, Social network analysis, Cambridge University Press, Cambridge, 1994
P. Pons, and M. Latapy, Computing communities in large networks using random walks, J. Graph Algorithms Appl., Vol. 10, No. 2, 2006, pp. 191–218
K.M. Hall, An r-dimensional quadratic placement algorithm, Management Science, Vol. 17, No. 3, 1970, pp. 219–229
L. Donetti and M.A. Munoz, Detecting network communities: a new systematic and efficient algorithm, J. Stat. Mech, Vol. 10, 2004, p. P10012
B. Yang, and J. Liu, Discovering global network communities based on local centralities, ACM Trans. on the Web, Vol. 2, No. 1, 2008, Article 9, pp. 1–32
W. Imrich and S. Klavzar, Product graphs: structure and recognition, Wiley, New York, 2000
B. Yang, and J. Liu, An efficient probabilistic approach to network community mining, Proc. of Joint Rough Set Symposium (JRS’07), 2007, pp. 267–275
B. Yang, J. Liu, D. Liu, An autonomy-oriented computing approach to community mining in distributed and dynamic networks, Autonomous Agent Multi-Agent System, Vol. 20, No. 2, 2010, pp. 123–157
J. Liu, X. Jin, K.C. Tsui. Autonomy oriented computing. Springer, Berlin, 2004
J. Liu, X. Jin, K.C. Tsui. Autonomy oriented computing (AOC): formulating computational systems with autonomous components, IEEE Trans. Systems Man Cybernetics, Part A: Systems and Humans, Vol. 35, No. 6, 2005, pp. 879–902
Acknowledgements
This work was funded by the National Natural Science Foundation of China under Grant Nos. 60773099, 60873149 and 60973088, the National High-Tech Research and Development Plan of China under Grant Nos. 2006AA10Z245 and 2006AA10A309, the Open Project Program of the National Laboratory of Pattern Recognition (NLPR), and the basic scientific research fund of Chinese Ministry of Education under Grant No. 200903177.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Yang, B., Liu, D., Liu, J. (2010). Discovering Communities from Social Networks: Methodologies and Applications. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_16
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7142-5_16
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7141-8
Online ISBN: 978-1-4419-7142-5
eBook Packages: Computer ScienceComputer Science (R0)