2011
Journal article  Open Access

A classification for community discovery methods in complex networks

Coscia M., Giannotti F., Pedreschi D.

Computer Science - Data Structures and Algorithms  Social and Information Networks (cs.SI)  Information Systems  Data Mining  FOS: Physical sciences  05C82 Small world graphs  Computer Science - Social and Information Networks  G.2.2 Graph Theory  FOS: Computer and information sciences  Computer Science Applications  Analysis  Physics - Physics and Society  Complex networks  Community discovery  Physics and Society (physics.soc-ph)  Data Structures and Algorithms (cs.DS) 

Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a 'user manual' for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research.

Source: Statistical analysis and data mining (Online) 4 (2011): 512–546. doi:10.1002/sam.10133

Publisher: John Wiley & Sons, Hoboken, N.J. , Stati Uniti d'America


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BibTeX entry
@article{oai:it.cnr:prodotti:199750,
	title = {A classification for community discovery methods in complex networks},
	author = {Coscia M. and Giannotti F. and Pedreschi D.},
	publisher = {John Wiley \& Sons, Hoboken, N.J. , Stati Uniti d'America},
	doi = {10.1002/sam.10133 and 10.48550/arxiv.1206.3552},
	journal = {Statistical analysis and data mining (Online)},
	volume = {4},
	pages = {512–546},
	year = {2011}
}