Efficient community identification in complex networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-12

AUTHORS

Mahadevan Vasudevan, Narsingh Deo

ABSTRACT

Complex networks are large, dynamic, random graphs modeled to replicate interactions among entities in real-world complex systems (e.g., the Internet, the World Wide Web, online social networks—Facebook, Twitter, etc., and the human connectome). These networks differ from the classical Erdös–Rényi random graphs in terms of network properties such as degree distribution, average distance and clustering. Existence of communities is one such property inherent to complex networks. A community may be defined informally as a locally dense subgraph, of a significant size, in a large globally sparse graph. Such communities are of interest in various disciplines, including graph theory, physics, statistics, sociology, biology, and linguistics. At least two different questions may be posed on the community structure in large networks: (1) given a network, detect or extract all (i.e., sets of nodes that constitute) communities, and (2) given a node in the network, identify the best community that the given node belongs to, if there exists one. Several algorithms have been proposed to solve the former problem, known as community discovery. The latter problem, known as community identification, has also been studied, but to a much smaller extent. Both these problems have been shown to be NP-complete, and a number of approximate algorithms have been proposed in recent years. In this paper, we discuss the various community definitions in the literature and analyze the algorithms for identifying communities. We propose an alternative definition of a community based on the average degree of the induced subgraph. Also, we propose a novel algorithm to identify community in complex networks based on maximizing the average degree. More... »

PAGES

345-359

References to SciGraph publications

  • 2012-06. Facebook as a Small World: a topological hypothesis in SOCIAL NETWORK ANALYSIS AND MINING
  • 1979-04. Cliques, clubs and clans in QUALITY & QUANTITY
  • 2005. Stochastic Local Clustering for Massive Graphs in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2012-06. Modeling blogger influence in a community in SOCIAL NETWORK ANALYSIS AND MINING
  • 2001-03. Exploring complex networks in NATURE
  • 2004-03. Detecting community structure in networks in THE EUROPEAN PHYSICAL JOURNAL B
  • 1999-09. Internet: Diameter of the World-Wide Web in NATURE
  • 1949-06. A method of matrix analysis of group structure in PSYCHOMETRIKA
  • 1998-06. Collective dynamics of ‘small-world’ networks in NATURE
  • 2003-09. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations in BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY
  • 2012-09. Context-sensitive detection of local community structure in SOCIAL NETWORK ANALYSIS AND MINING
  • 2001-05. Lethality and centrality in protein networks in NATURE
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    http://scigraph.springernature.com/pub.10.1007/s13278-012-0077-5

    DOI

    http://dx.doi.org/10.1007/s13278-012-0077-5

    DIMENSIONS

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