A novel cosine distance for detecting communities in complex networks

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

doi:10.1016/j.physa.2015.05.101

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…This algorithm is characterized by rapid clustering, easy implementation, and effective classification in large-scale dataset, and has been widely applied for community detection in complex networks. Additionally, the k -means clustering algorithm shows low time complexity compared to clustering methods based on centrality and similarity [ 19 , 20 , 21 ]. Nevertheless, conventional k -means clustering algorithms have several limitations [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

Cai

Zeng

Wang

et al. 2019

Entropy

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Community detection in networks plays a key role in understanding their structures, and the application of clustering algorithms in community detection tasks in complex networks has attracted intensive attention in recent years. In this paper, based on the definition of uncertainty of node community belongings, the node density is proposed first. After that, the DD (the combination of node density and node degree centrality) is proposed for initial node selection in community detection. Finally, based on the DD and k-means clustering algorithm, we proposed a community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM). The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers. Additionally, DDJKM can reduce the iteration times in the clustering process and the over-short distances between the initial cluster centers can be avoided by calculating the node similarity. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the DDJKM is practical a approach for the detection of communities with large network datasets.

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Section: Introductionmentioning

confidence: 99%

Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

Cai

Zeng

Wang

et al. 2019

Entropy

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“…In the past decade, many researchers have drawn increasing attention to dynamical analysis of complex dynamical networks due to a variety of their application fields, such as biology, physics, mathematics, sociology and so on [1][2][3][4][5][6]. On the basis of complex network models, the complex dynamical networks have been extensively investigated, especially in the interaction between the overall structure and complexity, and the local dynamical properties of the coupled nodes.…”

Section: Introductionmentioning

confidence: 99%

Pinning and adaptive synchronization of fractional-order complex dynamical networks with and without time-varying delay

Wang

Ruan

et al. 2018

Adv Differ Equ

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The synchronization problem for a class of fractional-order complex dynamical networks with and without time-varying delay is investigated in this paper. By utilizing generalized Barbalat's lemma, Razumikhin-type stability theory and matrix inequality technique, some sufficient criteria ensuring synchronization under pinning control and pinning adaptive feedback control are derived. Finally, three numerical simulations are presented to demonstrate the effectiveness of the obtained results.

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“…All these algorithms are time consuming due to the need of calculating modularity repeatedly. Conversely, clustering algorithms based on the degree centrality and similarity have small time complexity [17–19]. Obviously, nodes with higher degrees have more opportunities to be connected by other nodes, so the degree is taken as an index of centrality.…”

Section: Introductionmentioning

confidence: 99%

Efficient community detection method based on attribution of nodes in complex network

Cai

Song

Zeng

et al. 2018

J. eng.

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A novel cosine distance for detecting communities in complex networks

Cited by 11 publications

References 33 publications

Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

Pinning and adaptive synchronization of fractional-order complex dynamical networks with and without time-varying delay

Efficient community detection method based on attribution of nodes in complex network

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