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.
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
Hong Kong Baptist University（香港浸会大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
Querying Cohesive Subgraph regarding Span-Constrained Triangles on Temporal Graphs with Dynamic Index Maintenance
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Computer Science, Wuhan University（武汉大学计算机学院）；The Hong Kong University of Science and Technology（香港科技大学）
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications（重庆邮电大学网络空间安全与信息法学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Chengdu University of Technology（成都理工大学）；School of Software Engineering, Chengdu University of Information Technology（成都信息工程大学软件工程学院）；School of Computer and Information Science, Chongqing Normal University（重庆师范大学计算机与信息科学学院）
