Detecting overlapping communities in networks via dominant label propagation

Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks....

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Published inChinese physics B Vol. 24; no. 1; pp. 551 - 559
Main Author 孙鹤立 黄健斌 田勇强 宋擒豹 刘怀亮
Format Journal Article
LanguageEnglish
Published 2015
Subjects
Algorithms
Communities
Computer simulation
Labels
Mathematical analysis
Mathematical models
Networks
Voting
传播算法
复杂网络
显性
标签
测重
社区
群落结构
计算复杂性
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ISSN1674-1056
2058-3834
1741-4199
DOI10.1088/1674-1056/24/1/018703

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Summary:Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.
Bibliography:Community detection is an important methodology for understanding the intrinsic structure and function of a realworld network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm（Abbreviated as DLPA）, to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.
Sun He-Li,Huang Jian-Bin,Tian Yong-Qiang,Song Qin-Bao,Liu Huai-Liang（ 1. Department of Computer Science and Technology, Xi＇ an Jiaotong University, Xi＇ an 710049, China ;2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China ;3. School of Software, Xidian University, Xi＇an 710071, China ;4. School of Economics and Management, Xidian University, Xi＇ an 710071, China）
overlapping community detection; dominant label propagation; complex network
11-5639/O4
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/24/1/018703