论文标题
在社交媒体网络中发现有趣的子图
Discovering Interesting Subgraphs in Social Media Networks
论文作者
论文摘要
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质图。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“背景”图，然后自动发现与背景图在结构和内容上明显不同的子图。该技术结合了图表上的\ texttt {group-by}操作的概念，并概念主观有趣，从而自动发现有趣的子图。我们对社会政治数据库的实验显示了我们技术的有效性。
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.
