Paper Info
Title
Local community detection by the nearest nodes with greater centrality.
Abstract
Most community detection algorithms require the global information of the networks. However, for large scale complex networks, the global information is often expensive and even impossible to obtain. Therefore, local community detection is of tremendous significance. In this paper, a new local community detection algorithm based on NGC nodes, named LCDNN, is proposed. For any node, its NGC node refers to the nearest node with greater centrality. In the LCDNN, local community C initially consists of the given node, v. Then, the remaining nodes are added to the local community one by one, and the added node should satisfy: 1) its NGC node is in C, or it is the NGC node of the center node of C; and 2) the fuzzy relation between the node and its NGC node is the largest; 3) the fuzzy relation is no less than half of the average fuzzy relation of the current local network. The experimental results on ten real-world and synthetic networks demonstrate that LCDNN is effective and highly competitive. Concurrently, LCDNN can also be extended for multiscale local community detection, and experimental results are provided to demonstrate its effectiveness.
Year
DOI
Venue
2020
10.1016/j.ins.2020.01.001
Information Sciences
Keywords
Field
DocType
Social network,Community detection,Local community detection,Multiscale local community detection
Local community,Global information,Fuzzy logic,Centrality,Theoretical computer science,Complex network,Artificial intelligence,Local area network,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
517
0020-0255
1
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Wenjian Luo135640.95
Nannan Lu2146.24
Li Ni384.18
Wenjie Zhu431.40
Weiping Ding527844.96