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An Adaptive Approximation Algorithm for Community Detection in Social Network

机译:社交网络中社区检测的自适应近似算法

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Social network is one of the most important complex networks, which aims to describe the interactive relationship among a group of active actors that represent different kind of structure. Many systems in the real world such as human societies and different types of components can be modeled as social networks. We can represent such a network in terms of graphical community. Social Network Analysis provides inherent research due to success of social media sites and social content sharing facility. Social Network Analysis provides key terms to provide platform for industry to generate survey of product and facilitate to introduce new innovation ideas to public entity. Now a day, as increase the use of social media sites provide the entrepreneurs and user to define new concept of community creation that represents the relationship of users that might be interested in same kind of activity. To create such communities introduce new research area for researcher. This community detection is different from traditional clustering. In This paper, we propose new algorithm for community detection in social network to get some meaningful and important information.
机译:社交网络是最重要的复杂网络之一,旨在描述代表不同类型结构的活跃参与者群体之间的互动关系。可以将现实世界中的许多系统(例如人类社会和不同类型的组件)建模为社交网络。我们可以用图形社区来表示这样的网络。由于社交媒体网站和社交内容共享工具的成功,社交网络分析提供了固有的研究。社交网络分析提供了关键术语,为行业提供平台来进行产品调查并促进将新的创新思想引入公共实体。如今,随着社交媒体网站使用的增加,企业家和用户可以定义社区创建的新概念,该概念代表了可能对相同活动感兴趣的用户之间的关系。为了创建这样的社区,为研究人员介绍新的研究领域。这种社区检测不同于传统的群集。本文提出了一种新的社交网络社区检测算法,以获取有意义的重要信息。

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