Clustering algorithm for community detection in complex network: A comprehensive review

被引：0
作者：
Agrawal S. [1 ]
Patel A. [2 ]
机构：
[1] CSE Department, Institute of Technology, Nirma University, Ahmedabad
[2] CMPICA, CHARUSAT University, Changa
来源：
Recent Advances in Computer Science and Communications | 2020年 / 13卷 / 04期
关键词：
Collaborative similarity; Community detection; Complex network; Data set of community detections; Graph clustering; Vertex similarity;
D O I：
10.2174/2213275912666190710183635
中图分类号：
学科分类号：
摘要：
Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed. © 2020 Bentham Science Publishers.
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页码：542 / 549
页数：7
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