Glossary
- SN:
-
Social network
- TSN:
-
Temporal social network
Definition
Evolution of a particular social community can be represented as a sequence of events (changes) following each other in the successive timeframes within the temporal social network. In other words, the evolution is described by identified group transformations from time T i to T i+1 (i is the period index).
There are several definitions of possible events.
Asur et al. distinguish five possible events that may happen to groups, i.e., they may dissolve, form, continue, merge, and split (Asur et al. 2007).
Pala et al. identify six distinct transformations: growth, contraction, merging, splitting, birth, and death (Palla et al. 2007).
Bródka et al., in turn, describe seven noticeable event types: continuing, shrinking, growing, splitting, merging, dissolving, and forming (Bródka et al. 2013).
Some other different...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asur S, Parthasarathy S, Ucar D (2007) An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: KDD 2007, San Jose. ACM, pp 913–921
Barabasi AL, Jeong H, Neda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Phys A 311:590–614
Bródka P, Saganowski S, Kazienko P (2013) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min. 3(1):1–14
Bródka P, Saganowski S, Kazienko P (2012) Predicting group evolution in the social network. In: SocInfo 2012, Lausanne. Lecture notes in computer science. Springer
Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: KDD 2006, Philadelphia. ACM, pp 554–560
Dorogovtsev SN, Mendes JFF (2003) Evolution of networks: from biological nets to the internet and WWW. Oxford University Press, Oxford
Falkowski T, Bartelheimer J, Spiliopoulou M (2006) Mining and visualizing the evolution of subgroups in social networks. In: Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence (WI'06), Hong Kong, pp 52–58
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174
Ganti V, Gehrke J, Ramakrishnan R, Loh W-Y (2002) A framework for measuring differences in data characteristics. J Comput Syst Sci 64:542–578
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826
Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: ASONAM, Odense, pp 176–183
Kawadia V, Sreenivasan S (2012) Online detection of temporal communities in evolving networks by estrangement confinement. arXiv:1203.5126v1
Kim MS, Han J (2009) A particle-and-density based evolutionary clustering method for dynamic networks. In: VLDB 2009, Lyon. ACM, pp 622–633
Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311:88–90
Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:49
Lin YR, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: WWW 2008, Beijing. ACM, pp 685–694
Mucha PJ, Richardson T, Macon K, Porter MA, Onnela J-P (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878
Oliveira MCM, Gama J (2010) Bipartite graphs for monitoring clusters transitions. In: Proceedings of the 9th international conference on intelligent data analysis, Tucson, pp 114–124
Palla G, Barabási AL, Vicsek T (2007) Quantifying social group evolution. Nature 446:664–667
Saganowski S, Bródka P, Kazienko P (2012) Influence of the dynamic social network timeframe type and size on the group evolution discovery. In: ASONAM 2012, Istanbul. IEEE Computer Society, pp 678–682
Sarkar P, Moore AW (2005) Dynamic social network analysis using latent space models. SIGKDD Explor Newsl 7:31–40
Spiliopoulou M, Ntoutsi I, Theodoridis Y, Schult R (2006) Monic: modeling and monitoring cluster transitions. In: KDD 2006, Philadelphia, pp 706–711
Sun J, Papadimitriou S, Yu PS, Faloutsos C (2007) GraphScope: parameter-free mining of large time-evolving graphs. In: KDD 2007, San Jose. ACM, pp 687–696
Takaffoli M, Sangi F, Fagnan J, Zäíane OR (2011) Community evolution mining in dynamic social networks. Procedia – Soc Behav Sci 22:49–58
Zygmunt A, Bródka P, Kazienko P, Koźlak J (2012) Key person analysis in social communities within the blogosphere. J Univers Comput Sci 18(4): 577–597
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Bródka, P., Saganowski, S., Kazienko, P. (2014). Community Evolution. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_223
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6170-8_223
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6169-2
Online ISBN: 978-1-4614-6170-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering