Algorithms and Applications for Community Detection in Weighted Networks

Lu, Zongqing; Sun, Xiao; Wen, Yonggang; Cao, Guohong; Porta, Thomas La

doi:10.1109/tpds.2014.2370031

Cited by 99 publications

(50 citation statements)

References 24 publications

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“…In this section, we present some representative numerical results to validate the effectiveness of MMC comparing with two other schemes: Conductance [17] and COPRA [14], both of which can detect overlapping communities. We use LFR benchmark [18] proposed by Lancichinetti et al to test overlapping community detection algorithms.…”

Section: Methodsmentioning

confidence: 99%

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An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

2016

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Abstract. Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMC)to find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

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Section: Methodsmentioning

confidence: 99%

“…During the detection of community structure, the belonging degree proposed in [17] between a node v and a community C is defined as:…”

Section: Belonging Degreementioning

confidence: 99%

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

2016

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“…Conductance-based community detection algorithm has been proposed by Lu et al [13]. They have used data forwarding algorithm for delay tolerant networks and a worm containment strategy for online social networks based on intra-centrality and inter-centrality metrics.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Multi-Objective Overlapping Communities within Social Networks using a Socially Inspired Metaheuristic Algorithm

Özbay¹,

Alataş²

2017

ijcna

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-Frequently studied structural property of networks is community structure which is described as a group of users. User interactions inside the group are more than those outside the group. Communities in networks may be overlapped as users belong to multiple groups at once. This paper proposes a new socially inspired metaheuristic search and optimization algorithm, Parliamentary Optimization Algorithm (POA), to acquire promising solutions to overlapping community detection problems considering multiple objectives. The salient and unique feature of this work is that for the first time POA has been designed as a multi-objective search method for overlapping community detection. There is not any work about multi-objective overlapping community detection problem in the related literature. For this reason, simulation results of the proposed algorithm have not been compared with any results of works. The experimental studies on both artificial and real world social networks indicate that the POA ensures beneficial results for defining multi-objective overlapping community structure. A novel and interesting application area of POA has been introduced with this work. Parallel and distributed versions of social based POA with optimized parameters may also be efficiently designed and used for different social network problems.

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“…Hence, community detection is referred often as graph clustering problem. Much prior works has been done on clustering as well as community detection in several domains (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008;Boykov & Funka-Lea, 2006;Grady, 2006;He & Chen, 2015;Jeevan et al, 2011;Kanawati, 2011;Li & Wu, 2015;Liu & juan Ban, 2015;Lu, Sun, Wen, Cao, & La Porta, 2015;Shah & Zaman, 2010;Xu, Yuruk, Feng, & Schweiger, 2007;Wei et al, 2015). During community exploration process, properties of network elements are generally analyzed in three levels of abstraction.…”

Section: Related Workmentioning

confidence: 99%

Investigating community structure in perspective of ego network

Biswas

2015

Expert Systems with Applications

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Algorithms and Applications for Community Detection in Weighted Networks

Cited by 99 publications

References 24 publications

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

Discovery of Multi-Objective Overlapping Communities within Social Networks using a Socially Inspired Metaheuristic Algorithm

Investigating community structure in perspective of ego network

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