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Models for Community Dynamics

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Encyclopedia of Social Network Analysis and Mining

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Synonyms

Community evolution; Dynamic communities analysis; Mining time-evolving graphs; Temporal social network analysis

Glossary

Autonomy-Oriented Computing:

A method of self-organized computability of autonomous entities

Community Detection:

A method to find a set of nodes which are densely connected internally and less connected externally

Community Dynamics:

A way to analyze evolving communities

Community Evaluation:

A way to measure the identified communities

Complex Network:

A network with a nontrivial structure

Game Theory:

A strategy or mathematical model to deal with the conflict and cooperation problem among agents

Graph:

A set of nodes and edges

Multiagent System:

A distributed computing systems with multiple intelligent agents

Multimode Network:

A network with multiple types of nodes and connections

Single-Mode Network:

A network with one type of nodes and the same type of connections

Social Network:

A social structure consisted of social entities and their interaction

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References

  • Abello J, Resende M, Sudarsky S (2002) Massive quasi-clique detection. In: LATIN 2002: theoretical informatics. Springer, Berlin, pp 598–612

    Chapter  Google Scholar 

  • Alvari H, Hajibagheri A, Sukthankar G (2014) Community detection in dynamic social networks: a game-theoretic approach. In: Advances in social networks analysis and mining (ASONAM), 2014 IEEE/ACM international conference on IEEE, Beijing, pp 101–107.

    Google Scholar 

  • Bu Z, Wu Z, Cao J, Jiang Y (2016) Local community mining on distributed and dynamic networks from a multiagent perspective. IEEE Trans Cybern 46(4):986–999

    Article  Google Scholar 

  • Chen W, Liu Z, Sun X, Wang Y (2010) A game-theoretic framework to identify overlapping communities in social networks. Data Min Knowl Disc 21(2):224–240

    Article  MathSciNet  Google Scholar 

  • Chi Y, Song X, Zhou D, Hino K, Tseng BL (2007) Evolutionary spectral clustering by incorporating temporal smoothness. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Jose, California, pp 153–162

    Google Scholar 

  • Feng W, Wang J (2012) Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, ACM, New York, NY, pp 1276–1284

    Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Physics Reports. Elsevier, Amsterdam, 486, 75–174

    Google Scholar 

  • Granell C, Darst RK, Arenas A, Fortunato S, Gómez S (2015) Benchmark model to assess community structure in evolving networks. Phys Rev E 92(1):012,805

    Article  Google Scholar 

  • Handcock MS, Raftery AE, Tantrum JM (2007) Model-based clustering for social networks. J R Stat Soc Ser A 127(2):301–354

    Article  MathSciNet  Google Scholar 

  • Ji M, Han J, Danilevsky M (2011) Ranking-based classification of heterogeneous information networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, San Diego, California, pp 1298–1306

    Google Scholar 

  • Jiang Y, Jiang J (2014) Understanding social networks from a multiagent perspective. IEEE Trans Parallel Distrib Syst 25(10):2743–2759

    Article  Google Scholar 

  • Karrer B, Newman MEJ (2011) Stochastic blockmodels and community structure in networks. Phys Rev E 83:016,107

    Article  MathSciNet  Google Scholar 

  • Kemp C, Tenenbaum JB, Griffiths TL, Yamada T, Ueda N (2006) Learning systems of concepts with an infinite relational model. In: Proceedings of the national conference on artificial intelligence, AAAI Press, Boston, Massachusetts. vol 21, p 381

    Google Scholar 

  • Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49:291–307

    Article  MATH  Google Scholar 

  • Lin Y, Chi Y, Zhu S, Sundaram H, Tseng BL (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data 3(2.) Article 8

    Google Scholar 

  • Lu Z, Sun X, Wen Y, Cao G, La Porta T (2015) Algorithms and applications for community detection in weighted networks. IEEE Trans Parallel Distrib Syst 26(11):2916–2926

    Article  Google Scholar 

  • Mokken R (1979) Cliques, clubs and clans. Qual Quant 13:161–173

    Article  Google Scholar 

  • Newman ME (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci 98(2):404–409

    Article  MATH  MathSciNet  Google Scholar 

  • Newman M, Girvan M (2004) Finding and evaulating community structrue in networks. Phys Rev E 69(2):026,113

    Article  Google Scholar 

  • Palla G, Derenyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:814–818

    Article  Google Scholar 

  • Palla G, Barabasi AL, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667

    Article  Google Scholar 

  • Scott J (2000) Social network analysis: a handbook. Sage, Los Angeles, California

    Google Scholar 

  • Slater PB (2008) Established clustering procedures for network analysis. Technical report. arXiv:0806.4168

    Google Scholar 

  • Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synth Lect Data Min Knowl Disc 3(2):1–159

    Article  Google Scholar 

  • Sun Y, Yu Y, Han J (2009) Ranking-based clustering of heterogeneous information networks with star network schema. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Paris, pp 797–806

    Google Scholar 

  • Sun Y, Norick B, Han J, Yan X, Yu PS, Yu X (2012) Interating meta-path selection with user-guided object clustering in heterogeneous information networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. Morgan & Claypool, San Rafael, California

    Google Scholar 

  • Tang L, Liu H (2010) Community detection and mining in social media. Morgan & Claypool, San Rafael, California

    Google Scholar 

  • Tang L, Liu H, Zhang J (2012a) Identifying evolving groups in dynamic multimode networks. IEEE Trans Knowl Data Eng 24(1):72–85

    Article  Google Scholar 

  • Tang L, Wang X, Liu H (2012b) Community detection via heterogeneous interaction analysis. Data Min Knowl Disc 25:1–33

    Article  MathSciNet  Google Scholar 

  • Tantipathananandh C, Berger-Wolf T (2009) Constant-factor approximation algorithms for identifying dynamic communities. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, pp 827–836

    Google Scholar 

  • Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. In: Proceedings of 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Jose, California, pp 717–726

    Google Scholar 

  • Wu J, Xiong H, Chen J (2009) Adapting the right measures for k-means clustering. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, world text mining conference. pp 877–886. ACM, New York, NY

    Google Scholar 

  • Xu KS, Hero III AO (2013) Dynamic stochastic blockmodels: statistical models for time-evolving networks. In: International conference on social computing, behavioral-cultural modeling, and prediction. Springer, pp 201–210. Washington, DC

    Google Scholar 

  • Yang Y, Chen K (2011) Temporal data clustering via weighted clustering ensemble with different representations. IEEE Trans Knowl Data Eng 23(2):307–320

    Article  Google Scholar 

  • Yang T, Chi Y, Zhu S, Gao Y, Jin R (2009) A bayesian approach toward finding communities and their evolutions in dynamic social networks. In: Proceedings of the SAIM of the data mining. pp 990–1001. Society for Industrial and Applied Mathematics, Philadelphia, PA

    Google Scholar 

  • Yang B, Liu J, Liu D (2010) An autonomy-oriented computing approach to community mining in distributed and dynamic networks. Auton Agent Multi-Agent Syst 20(2):123–157

    Article  Google Scholar 

Download references

Acknowledgment

This research has been partially supported by National Natural Science Foundation of China under Grants 71571093, 71372188, and 61502222, National Center for International Joint Research on E-Business Information Processing under Grant 2013B0135, Industry Projects in Jiangsu S&T Pillar Program under Grant BE2014141, and Key/- Surface Projects of Natural Science Research in Jiangsu Provincial Colleges and Universities under Grants 12KJA520001, 14KJA520001, 14KJB520015, 15KJB520012, and 15KJB520011.

Author information

Authors and Affiliations

  1. Advanced Analytics Institute, University of Technology, Sydney, Broadway, NSW, Australia

    Guandong Xu

  2. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China

    Zhiang Wu & Jie Cao

  3. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Haicheng Tao

Authors
  1. Guandong Xu
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  2. Zhiang Wu
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  3. Jie Cao
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  4. Haicheng Tao
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Corresponding author

Correspondence to Guandong Xu .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

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

    Irwin King

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Jie Tang

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Xu, G., Wu, Z., Cao, J., Tao, H. (2017). Models for Community Dynamics. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_180-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_180-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

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