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Dense Subgroup Identifying in Social Network

机译:社交网络中的密集子群识别

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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提供有关社交网络的潜在内部结构的有价值的知识,并且最近在图挖掘社区中已经见证了挖掘大型网络中频繁出现的相干子图的几种应用并受到了相当大的关注。但是,某些主要参与者并不总是出现在集团中,因此,集团检测无法识别社交网络中的某些核心成员。在本文中,我们通过对稠密子图的节点附加距离限制来定义稠密子图问题的一般化,实际上它是准clique。我们提出了一种基于稠密子图定义的准古怪检测算法,以及一种基于同步思想的新的优化技术,可以从稠密子图上剔除那些无用和多余的外星人。所提出的方法可以发现准集团中未显示的准公司和核心参与者。

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