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| 基于边密度聚类的重叠社区发现算法 | |
| 引用本文： | 郭昆,陈而宝,郭文忠. 基于边密度聚类的重叠社区发现算法[J]. 模式识别与人工智能, 2018, 31(8): 693-703. DOI: 10.16451/j.cnki.issn1003-6059.201808002 |
| 作者姓名： | 郭昆 陈而宝 郭文忠 |
| 作者单位： | 1.福州大学 数学与计算机科学学院 福州 350116 2.福州大学 福建省网络计算与智能信息处理重点实验室 福州 350116 3.福州大学 空间数据挖掘与信息共享教育部重点实验室 福州 350116 |
| 基金项目： | 国家自然科学基金项目(No.61300104,61300103,61672158)、福建省自然科学基金项目(No. 2013J01230,2014J01232)、福建省高校杰出青年科学基金项目(No.JA12016)、福建省高等学校新世纪优秀人才支持计划项目(No.JA13021)、福建省杰出青年科学基金项目(No.2014J06017,2015J06014)、福建省科技创新平台计划项目(2009J1007,2014H2005)、福建省高校产学合作项目(No.2014H6014,2017H6008)、海西政务大数据应用协同创新中心资助 |
| 摘 要： | 基于边聚类的社区发现算法以边为聚类对象,自然发现重叠社区,但也存在生成的社区集边界归属模糊、社区结构过度重叠等问题.基于此种情况,文中提出基于边密度聚类的重叠社区发现算法.首先,以边为研究对象,通过密度聚类检测连接紧密的核心边社区.然后,根据边界边归属策略将边界边划分到离它最近的核心边社区.针对孤立边,提出基于边的度与边的社区归属的孤立边处理策略,进一步处理未划分的孤立边,避免社区结构过度重叠的问题.最后,将边社区还原为节点社区,实现重叠社区的发现.在人工数据集和真实数据集上的实验表明,文中算法可以快速准确地检测复杂网络中的重叠社区. |
| 关 键 词： | 重叠社区 复杂网络 边聚类 密度聚类 |
| 收稿时间： | 2018-04-03 |
Overlapping Community Detection Based on Edge Density Clustering |
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| GUO Kun,CHEN Erbao,GUO Wenzhong. Overlapping Community Detection Based on Edge Density Clustering[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(8): 693-703. DOI: 10.16451/j.cnki.issn1003-6059.201808002 | |
| Authors: | GUO Kun CHEN Erbao GUO Wenzhong |
| Affiliation: | 1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2.Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116 3.Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350116 |
| Abstract: | Community detection based on edge clustering is capable of detecting overlapping communities naturally. However, it engenders the problems of obscure belongingness of the nodes on community borders and the excessive overlap of communities. In this paper, an overlapping community detection based on edge density clustering(OCDEDC) algorithm is proposed. Firstly, density clustering based on edges is employed to extract core edge communities. Next, a partitioning strategy is designed to dispatch border edges to its closest core edge community. In addition, a strategy based on the degrees and community belongingness of edges is designed to handle the isolated edges, and thus the excessive overlap of communities is avoided. Finally, edge communities are transformed back into node communities as the output. Experiments on artificial and real datasets show that the proposed algorithm detects overlapping communities efficiently and effectively. |
| Keywords: | Overlapping Community Complex Network Edge Clustering Density Clustering |
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