2018
Journal article
Open Access
Community discovery in dynamic networks: A survey
Rossetti G., Cazabet R.
A.1 Temporal networks Social and Information Networks (cs.SI) [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] General Computer Science E.1 FOS: Physical sciences I.5.3 Computer Science - Social and Information Networks G.2.2 [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] Dynamic networks FOS: Computer and information sciences Physics - Physics and Society Theoretical Computer Science Community discovery Physics and Society (physics.soc-ph)
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a "user manual," this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.
Source: ACM computing surveys 51 (2018). doi:10.1145/3172867
Publisher: Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America
Appendix Methods in this category: (Bota et al., 2011; Bourqui et al., 2009; Brodka et al., 2013; Dhouioui and Akaichi, 2014; Greene et al., 2010; Hopcroft et al., 2004; I_lhan and O guducu, 2015; Palla et al., 2007; Rosvall and Bergstrom, 2010; Taka oli et al., 2011). Refer to Appendix A.1 for description of methods.
2. Iterative Core-nodes based approaches Methods in this category: (Alvari et al., 2014; Aynaud and Guillaume, 2010; Bansal et al., 2011; Gorke et al., 2010; Miller and Eliassi-Rad, 2009; Shang et al., 2014) Methods in this category: (Crane and Dempsey, 2015; Folino and Pizzuti, 2010; Gong et al., 2012; Gorke et al., 2013; Kawadia and Sreenivasan, 2012; Lin et al., 2008, 2009; Sun et al., 2010; Tang et al., 2008; Yang et al., 2009; Zhou et al., 2007) Refer to Appendix B.3 for description of methods. Methods in this category: (Himmel et al., 2016; Jdidia et al., 2007; Mucha et al., 2010; Viard et al., 2016) Refer to Appendix C.4 for description of methods.
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In (Viard et al., 2016), the notion of clique is generalized to link streams. A -clique is a set of nodes and a time interval such that all nodes in this set are pairwise connected at least once during any sub-interval of duration of the interval. The paper presents an algorithm able to compute all maximal (in terms of nodes or time interval) -cliques in a given link stream, for a given . A solution able to reduce the computational complexity has been proposed in (Himmel et al., 2016).
2. Iterative Core-nodes based approaches Methods in this category: (Alvari et al., 2014; Aynaud and Guillaume, 2010; Bansal et al., 2011; Gorke et al., 2010; Miller and Eliassi-Rad, 2009; Shang et al., 2014) Methods in this category: (Crane and Dempsey, 2015; Folino and Pizzuti, 2010; Gong et al., 2012; Gorke et al., 2013; Kawadia and Sreenivasan, 2012; Lin et al., 2008, 2009; Sun et al., 2010; Tang et al., 2008; Yang et al., 2009; Zhou et al., 2007) Refer to Appendix B.3 for description of methods. Methods in this category: (Himmel et al., 2016; Jdidia et al., 2007; Mucha et al., 2010; Viard et al., 2016) Refer to Appendix C.4 for description of methods.
Url Papers (Goldberg et al., 2011; Ma and Huang, 2013; Miller
http://goo.gl/5R9ozh aTnadka oli et al., 2011; Tang Eliassi-Rad, 2009; et al., 2008; Wang et al., 2008)
http://arxiv.org (Wang et al., 2008) (Gorke et al., 2013; I_lhan and Oguducu, 2015; Shang et al., 2014)
http://imdb.com (Goldberg et al., 2011) Dataset Url Papers (Falkowski et al., 2008; Ferry and Bumgarner, 2012;
ENRON https://goo.gl/gQqo8l 2F0o1li4n;oLiaentdalP.,iz2z0u1t1i,; S2h0a1n0g, et al., 2014; Tang et al., 2008; Wang et al., 2008)
KIT https://goo.gl/gQqo8l (Gorke et al., 2013) (Folino and Pizzuti, 2010,
Mobile phone - 2014; Gong et al., 2012; Guo et al., 2014)
wiki-Vote https://goo.gl/pzhst2 (Shang et al., 2014) Senate/Court Voting - (Crane and Dempsey, 2015) Thiers http://goo.gl/jhAcY8 (Viard et al., 2016) CAIDA http://goo.gl/72y9iT (2M00i9ll)er and Eliassi-Rad, Facebook http://goo.gl/8J18cw (Rossetti et al., 2016a) Weibo http://goo.gl/bcoQiy (Rossetti et al., 2016a) Digg http://goo.gl/ybKQS2 (2Z0a15k)rzewska and Bader, Slashdot http://goo.gl/FGgEiM (2Z0a15k)rzewska and Bader, Agarwal, M. K., K. Ramamritham, and M. Bhide, 2012, Proceedings of the VLDB Endowment 5(10), 980.
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In (Viard et al., 2016), the notion of clique is generalized to link streams. A -clique is a set of nodes and a time interval such that all nodes in this set are pairwise connected at least once during any sub-interval of duration of the interval. The paper presents an algorithm able to compute all maximal (in terms of nodes or time interval) -cliques in a given link stream, for a given . A solution able to reduce the computational complexity has been proposed in (Himmel et al., 2016).
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BibTeX entry
@article{oai:it.cnr:prodotti:384934,
title = {Community discovery in dynamic networks: A survey},
author = {Rossetti G. and Cazabet R.},
publisher = {Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America},
doi = {10.1145/3172867 and 10.48550/arxiv.1707.03186},
journal = {ACM computing surveys},
volume = {51},
year = {2018}
}CNR authorsLaboratories
0000-0003-3373-1240CNR ExploRA
ISTI Repository
DOI
ISTI Repository
DOI
10.1145/3172867
10.48550/arxiv.1707.03186
arXiv.org e-Print Archive
ACM Computing Surveys
dl.acm.org
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