A framework of mapping undirected to directed graphs for community detection

Sun, Peng; Gao, Lill

doi:10.1016/j.ins.2014.10.069

Cited by 14 publications

(2 citation statements)

References 44 publications

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“…Peng and Lill have proposed a framework for mapping the community detection algorithm from undirected to the directed network. They have applied modularity optimization technique for obtaining optimal partitioning of the network [28].…”

Section: Related Workmentioning

confidence: 99%

Large Scale Community Detection Using a Small World Model

et al. 2017

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Abstract:In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones.

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Section: Related Workmentioning

confidence: 99%

Large Scale Community Detection Using a Small World Model

et al. 2017

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“…Based on the concept of modularity, the problem of community detection becomes equivalent to modularity optimization. Therefore, several methods have been proposed to find the optimal solution with the lowest computational cost [3,6,10,15,17,33,32,40]. Fortunato and Barthélemy [12] found that such modularity contains an intrinsic scale that depends on the total size of links in a network.…”

Section: Introductionmentioning

confidence: 99%