Overlapping community detection using neighborhood ratio matrix

Eustace, Justine; Wang, Xingyuan; Cui, Yaozu

doi:10.1016/j.physa.2014.11.039

Cited by 52 publications

(25 citation statements)

References 36 publications

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“…Finally, they realized community detection. Eustace et al [20] proposed the NRATIO algorithm based on a vertex neighborhood matrix to detect communities, but this method does not hold in non-dynamic large networks. Yoshida [21] constructed a similarity matrix using topological similarity and feature similarity; they then utilized the spectral clustering method to detect communities.…”

Section: Related Workmentioning

confidence: 99%

A graph clustering method for community detection in complex networks

Zhou

et al. 2017

Physica A: Statistical Mechanics and its Applications

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Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

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Section: Related Workmentioning

confidence: 99%

A graph clustering method for community detection in complex networks

Zhou

et al. 2017

Physica A: Statistical Mechanics and its Applications

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“…For network in Fig.1, our algorithm computes the structural similarity of each edge and the weighted network is shown in Fig.2. The structural , e(5, 7), e (7,8) and e (7,11) are smallest of all. They certainly will be deleted and the deletion of e(4, 7) and e(5, 7) will cause the network splits into two communities.…”

Section: Motivating Examplementioning

confidence: 99%

LED: A fast overlapping communities detection algorithm based on structural clustering

Wang

Tang

et al. 2016

Neurocomputing

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Community detection in social networks is a fundamental task of complex network analysis. Community is usually regarded as a functional unit. Networks in real world more or less have overlapping community structure while traditional community detection algorithms assume that one vertex can only belong to one community. This paper proposes an efficient overlapping community detection algorithm named LED (Loop Edges Delete). LED algorithm is based on Structural Clustering, which converts structural similarity between vertices to weights of network. The evaluations of the LED algorithm are conducted both from classical networks from literature and C-DBLP, which is a huge and real-life co-author social network in China. The results show LED is superior to other methods in accuracy, efficiency, comparing with FastModurity and GN algorithm.

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“…In this section, the proposed algorithm was tested on six classical real-life social networks including Friendship network [28] American college football teams network [29], Dolphin's association network [20], Pol-books network [30], Net-science network [17], Pol-blog network [31].…”

Section: Real-world Networkmentioning

confidence: 99%

“…This network originally has 3 communities shown inFig. 14because the books have been divided into three categories which include neutral, conservative and liberal[30].…”

mentioning

confidence: 99%

A novel cosine distance for detecting communities in complex networks

Wang

2015

Physica A: Statistical Mechanics and its Applications

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Overlapping community detection using neighborhood ratio matrix

Cited by 52 publications

References 36 publications

A graph clustering method for community detection in complex networks

A graph clustering method for community detection in complex networks

LED: A fast overlapping communities detection algorithm based on structural clustering

A novel cosine distance for detecting communities in complex networks

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