Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly different from the background graph. The technique combines the notion of a \texttt{group-by} operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs. Our experiments on a socio-political database show the effectiveness of our technique.

中文翻译：

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在社交媒体中发现有趣子图的查询驱动系统

社交媒体数据通常被建模为具有多种类型的节点和边缘的异构图。我们提出了一种发现算法，该算法首先根据用户的分析兴趣选择一个“背景”图，然后自动发现在结构和内容方面与背景图明显不同的子图。该技术将图上\ texttt {group-by}操作的概念与主观兴趣的概念结合在一起，从而自动发现了有趣的子图。我们在社会政治数据库中进行的实验表明了我们技术的有效性。