A FAST OVERLAPPING COMMUNITY DETECTION ALGORITHM BASED ON LABEL PROPAGATION AND SOCIAL NETWORK GRAPH CLUSTERING COEFFICIENT

Nguyen Hien Trinh, Doan Van Ban, Vu Vinh Quang, Cáp Thanh Tùng
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Abstract

Detecting community structure on social network has been an important and interesting issue on which many researchers have paid much attention and developed applications. Many graph clustering algorithms have been applied to find disjoint communities, i.e each node belongs to a single community. However, for social network in particular, public communication network in general, most of communities are not completely detached but they may be embedding, overlapping or crossing, that means certain nodes can belong to more than one community. Overlapping node plays a role of interface between communities and it is really interesting to study the community establishment of these nodes because it reflects dynamic behaviuor of participants.This article introduces the algorithm to find overlapping communities on huge social network. The proposed COPACN algorithm has been developed on the basis of label propagation, using advanced clustering coefficient to find overlapping communities on social network. Exprermental results on a set of popular, standard social networks and certain real network have shown the high speed and high effiency in finding overlapping structures.
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一种基于标签传播和社交网络图聚类系数的快速重叠社区检测算法
社交网络上的社区结构检测一直是一个重要而有趣的问题,受到许多研究者的关注并开发了应用。许多图聚类算法被用于寻找不相交的群体,即每个节点属于一个单一的群体。然而,对于社交网络,特别是公共通信网络,大多数社区并不是完全分离的,它们可能是嵌入的,重叠的或交叉的,这意味着某些节点可能属于多个社区。重叠节点作为社区之间的接口,反映了参与者的动态行为,因此研究重叠节点的社区建立是一个非常有趣的问题。本文介绍了在大型社交网络中寻找重叠社区的算法。本文提出的COPACN算法是在标签传播的基础上发展起来的,利用先进的聚类系数来寻找社会网络上重叠的社区。在一组流行的、标准的社交网络和某些真实网络上的实验结果表明,该方法可以快速高效地发现重叠结构。
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