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Dense Subgroup Identifying in Social Network

机译:社交网络中的密集子集

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摘要

The densest coherent sub graphs can provide valuable knowledge about the underlying internal structure of a social network, and mining frequently occurring coherent sub graphs of a large network has been witnessed several applications and received considerable attention in the graph mining community recently. However, some key players are not always appeared in the clique, therefore, clique detection could not identify some core members in social networks. In this paper, we define a generalization of the dense sub graph problem by an additional distance restriction to the nodes of the dense sub graph which is a quasi-clique in fact. We propose a new quasi-clique detection algorithm based on the definition of dense sub graph, and a novel optimization techniques based on idea of synchronization, which can prune the unpromising and redundant alien from the dense sub graph. The proposed methods could discover quasi-cliques and core players that are not shown in clique.
机译:最密集的相干子图可以为社交网络的底层内部结构提供有价值的知识,并且大网络的频繁发生的连贯子图已经见证了几种应用,并在最近的图形挖掘社区中得到了相当大的关注。但是,某些关键播放器并不总是出现在集团中,因此,Clique检测无法识别社交网络中的一些核心成员。在本文中,我们通过对致密子图的节点的额外距离限制来定义密集的子图问题的概念,其实际上是Quasi-Clique。我们提出了一种基于密集子图的定义的新的准集团检测算法,以及基于同步思想的新颖优化技术,可以从密集的子图中修剪不妥协和冗余的外星。所提出的方法可以发现在集团中未显示的准批变和核心玩家。

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