Local community detection by the nearest nodes with greater centrality

被引：48

作者：

Luo, Wenjian ^{[1
,2
]}

Lu, Nannan ^{[1
,2
]}

Ni, Li ^{[1
,2
]}

Zhu, Wenjie ^{[1
,2
]}

Ding, Weiping ^{[3
]}

机构：

[1] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China

[2] Univ Sci & Technol China, Anhui Prov Key Lab Software Engn Comp & Commun, Hefei 230027, Anhui, Peoples R China

[3] Nantong Univ, Sch Comp Sci & Technol, Nantong 226019, Peoples R China

来源：

INFORMATION SCIENCES | 2020年 / 517卷

基金：

中国国家自然科学基金;

关键词：

Social network; Community detection; Local community detection; Multiscale local community detection; COMPLEX NETWORKS;

D O I：

10.1016/j.ins.2020.01.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 multi-scale local community detection, and experimental results are provided to demonstrate its effectiveness. (C) 2020 Elsevier Inc. All rights reserved.

引用

页码：377 / 392

页数：16

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