Framework for Fast Identification of Community Structures in Large-Scale Social Networks

Leon-Suematsu, Yutaka I.; Yuta, Kikuo

doi:10.1007/978-1-4419-6287-4_9

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…Community detection is of great interest in the field of complex networks and its study has been subject of many works [1][2][3][4][5][6]. A consensual notion about the characterization of a community in a network is a subset of nodes with great internal density and low external density.…”

Section: Introductionmentioning

confidence: 99%

“…The 2 Mathematical Problems in Engineering work of Fortunato and Barthélemy is worth mentioning [17], which verifies that the modularity can fail in the identification of intuitive communities (for instance, cliques of nodes). This problem is broadly addressed in the literature [4,[18][19][20] and frequently related as the resolution limit problem. Another aspect which is important to point out in the use of modularity is the fact that some communities can show high modularity values, even with just few variations from random connections, as observed by Guimerà et al [21] and also discussed by Kehagias [22].…”

Section: Introductionmentioning

confidence: 99%

“…The discussion concerning the use of the modularity as a measure of the quality of partitions, introduced by the aspects previously mentioned, is of primary importance in the community detection area. Nevertheless, several works as [4,18,20] discuss that modularity is still a very appropriate measure for the assessment of community structures in networks and emphasize the importance of the investigation on methods for modularity maximization.…”

Section: Introductionmentioning

confidence: 99%

“…There is a great demand for computational methods that are capable of detecting community structure in large scale networks and this is currently one of the most important problems in the area of complex networks. Several works can be found in the literature with the purpose of proposing and studying such methods and, among them, [1,2,4,9,15,[23][24][25][26][27] can be cited.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Vieira

Xavier

Ebecken

et al. 2014

Mathematical Problems in Engineering

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Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Vieira

Xavier

Ebecken

et al. 2014

Mathematical Problems in Engineering

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“…Moreover a large number of methods to detect such communities can be found in literature. Community detection in large scale complex networks is a challenge task and many studies focusing this subject can be found in literature [24,30,3,20,8,10].…”

Section: Introductionmentioning

confidence: 99%

Modularity Based Hierarchical Community Detection in Networks

Vieira

Xavier

Ebecken

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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The organization of nodes in communities, i.e., groups of nodes with many internal connections and few external connections, is one of the main structural features of networks and community detection is one of the most challenging tasks in networks. The communities in networks can be observed in different levels and a great number of methods can be found in the literature in order to identify the hierarchical organization of the communities. This work proposes a methodology for the representation of the hierarchical organization of communities in complex networks based on the spectral method of Newman. The proposed methodology, in contrast to other traditional approaches found in the literature, use the modularity, one of the most adopted measures for the quality of communities, in order to define the distances between the communities in the network. The methodology provides, as output, a dendrogram in order to illustrate the hierarchical organization of communities in networks. The application of the methodology to large scale networks show that the hierarchical visualization enhances the understanding of the complex systems modelled by networks, providing a broader view of the community structures.

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A Comparison of Methods for Community Detection in Large Scale Networks

Vieira

Evsukoff

2013

Complex Networks

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Framework for Fast Identification of Community Structures in Large-Scale Social Networks

Cited by 5 publications

References 28 publications

Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Modularity Based Hierarchical Community Detection in Networks

A Comparison of Methods for Community Detection in Large Scale Networks

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