A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network

Bu, Zhan; Zhang, Chengcui; Xia, Zhengyou; Wang, Jiandong

doi:10.1016/j.knosys.2013.06.014

Cited by 87 publications

(36 citation statements)

References 24 publications

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“…In previous research, modularity has mainly been used to evaluate community detection [34,35]. In our paper, we introduce modularity to measure the strength of the connection between nodes in a group.…”

Section: Modularitymentioning

confidence: 99%

Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

Chen

Luo

Sun

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Section: Modularitymentioning

confidence: 99%

Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

Chen

Luo

Sun

et al. 2015

Physica A: Statistical Mechanics and its Applications

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“…The methods based on the coarse graining clump nodes sharing common attributes together in the same group/community and then consider the whole group as one single unit in the new networks. Some methods along this line include the box-covering technique [4], fractal skeleton [7], and traditional community detection techniques such as the Kernighan-Lin algorithm [11], latent space models [12], stochastic block models [13], and modularity optimization [14]. The differences between these methods ultimately come down to the precise definition of a community.…”

Section: Related Workmentioning

confidence: 99%

“…The methods based on the coarse graining [4,7,[11][12][13][14] clump nodes sharing common attributes together in the same group/community and then consider the whole group as one single unit in the new networks. However, there is often no clear statement on whether properties of the initial network should be preserved in the network of clusters [15].…”

Section: Introductionmentioning

confidence: 99%

Extracting Backbones from Weighted Complex Networks with Incomplete Information

Qian¹,

Lu³

et al. 2015

Abstract and Applied Analysis

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The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency-the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.

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“…Since community detection can help us understand the organization characteristics of complex networks [8,33], a large number of algorithms for community detection have been put forward in the past few years [3,4,[6][7][8]19,20,[25][26][27][37][38][39]41,42].…”

Section: Related Workmentioning

confidence: 99%

“…Over the years, many algorithms have been proposed to uncover community structure in complex networks [3,4,[6][7][8]19,20,[25][26][27][37][38][39]41,42]. Newman and Girvan [11,22,23] put forward an interesting work that solves the problem of community detection based on objective optimization.…”

Section: Introductionmentioning

confidence: 99%