Attributed Community Analysis: Global and Ego-centric Views
Attributed Community Analysis: Global and Ego-centric Views
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发表时间：
2016
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通讯作者：
Xin Huang;Hong Cheng;J. Yu
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作者：
Xin Huang;Hong Cheng;J. Yu
真实的世界实体及其关系的丰富信息的激增产生了一类图，即属性图，其中图的顶点与许多属性相关联。属性的集合可以由一系列关键字构成。在属性图中，发现具有同质属性值的密集连接组件的社区是非常有用的。根据不同的方面，社区分析任务可以分为全球网络范围和以自我为中心的个性化。全局网络范围的社区分析考虑整个网络，因此社区检测，即找到网络中的所有社区。另一方面，以自我为中心的个性化社区分析侧重于给定查询节点的局部邻域子图，从而进行社区搜索。在给定一组查询节点和属性的情况下，属性图中的社区搜索是以在线的方式在本地发现包含查询相关节点的有意义社区。在本文中，我们布里简单地综述了几种基于各种稠密子图的社区模型，同时也研究了社交圈，一种特殊的社区是由一个特定用户的一跳邻居网络中的朋友组成的。
The proliferation of rich information available for real world entities and their relationships gives rise to a type of graph, namely attributed graph, where graph vertices are associated with a number of attributes. The set of an attribute can be formed by a series of keywords. In attributed graphs, it is practically useful to discover communities of densely connected components with homogeneous attribute values. In terms of different aspects, the community analysis tasks can be categorized into global network-wide and ego-centric personalized. The global network-wide community analysis considers the entire network, such that community detection, which is to find all communities in a network. On the other hand, the ego-centric personalized community analysis focuses on the local neighborhood sub-graph of given query nodes, such that community search. Given a set of query nodes and attributes, community search in attributed graphs is to locally detect meaningful community containing query-related nodes in the online manner. In this work, we briefly survey several state-of-the-art community models based on various dense subgraphs, meanwhile also investigate social circles, that one special kind of communities are formed by friends in 1-hop neighborhood network for a particular user.