An upper approximation based community detection algorithm for complex networks

2017 ◽  
Vol 96 ◽  
pp. 103-118 ◽  
Author(s):  
Pradeep Kumar ◽  
Samrat Gupta ◽  
Bharat Bhasker
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Upper Approximation ◽  
Community Detection Algorithm

A fast local community detection algorithm in complex networks

2021 ◽  
Author(s):  
Zhikang Tang ◽  
Yong Tang ◽  
Chunying Li ◽  
Jinli Cao ◽  
Guohua Chen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Local Community Detection ◽  
Community Detection Algorithm

Constant community discovery in network based on consensus clustering

2014 ◽  
Vol 28 (19) ◽  
pp. 1450126
Author(s):  
Zongwen Liang ◽  
Athina Petropulu ◽  
Fan Yang ◽  
Jianping Li
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Consensus Clustering ◽  
Community Discovery ◽  
Structural And Functional Properties ◽  
Consensus Matrix ◽  
Internal Properties ◽  
Community Detection Algorithm ◽  
Vertex Order

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field.

scholarly journals An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Feifan Wang ◽  
Baihai Zhang ◽  
Senchun Chai ◽  
Yuanqing Xia
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Computational Costs ◽  
Detection Algorithms ◽  
Learning Machines ◽  
The Past ◽  
Real World Datasets ◽  
Learning Machine ◽  
Community Detection Algorithm

Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k-means clustering techniques, we propose a novel community detection method that surpasses traditional k-means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.

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