Producing a unified graph representation from multiple social network views
Producing a unified graph representation from multiple social network views
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DOI：
10.1145/2464464.2464471
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Derek Greene;P. Cunningham
中科院分区：
文献类型：
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作者：
Derek Greene;P. Cunningham
在许多社交网络中，在同一组用户之间将存在若干不同的链接关系。此外，属性或文本信息将与这些用户相关联，例如人口统计细节或用户生成的内容。对于许多数据分析任务，例如社区查找和数据可视化，提供多种异构类型的用户数据使分析过程更加复杂。我们提出了一种无监督的方法，用于集成多个数据视图，以产生一个统一的图形表示，基于来自每个视图的用户的k-最近邻集的组合。这些视图可以是基于关系的，也可以是基于特征的。所提出的方法进行了评估，在一些注释的多视图Twitter数据集，在那里它被证明是支持在数据中的底层社区结构的发现。
In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produce a single unified graph representation, based on the combination of the k-nearest neighbour sets for users derived from each view. These views can be either relation-based or feature-based. The proposed method is evaluated on a number of annotated multi-view Twitter datasets, where it is shown to support the discovery of the underlying community structure in the data.