Tree Decomposition Based Anomalous Connected Subgraph Scanning for Detecting and Forecasting Events in Attributed Social Media Networks Minglai Shao a,b , Peiyuan Sun a,b , Jianxin Li a,b,∗ , Qiben Yan c , Zhirui Feng a,b a Bejing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China b State Key Laboratory of Software Development Environment, Beihang University, Beijing, China c Computer Science and Engineering, Michigan State University, East Lansing, MI, USA Abstract Event detection and forecasting in social media networks, such as disease outbreak and air pollution event detection, have been formulated as an anomalous connected sub- graph detection problem. However, the huge search space and the sparsity of anomaly events make it difficult to solve this problem effectively and efficiently. This pa- per presents a general framework, namely anomalous connected subgraph scanning (GraphScan) which optimizes a large class of sophisticated nonlinear nonparametric scan statistic functions, to solve this problem in attributed social media networks. We first transform the sophisticated nonlinear nonparametric scan statistics functions into the Price-Collecting Steiner Tree (PCST) problem with provable guarantees for evalu- ating the significance of connected subgraphs to indicate the ongoing or forthcoming events. Then, we use tree decomposition technique to divide the whole graph into a set of smaller subgraph bags, and arrange them into a tree structure, through which we can reduce the search space dramatically. Finally, we propose an efficient approxima- tion algorithm to solve the problem of anomalous subgraph detection using the tree of bags. With two real-world datasets from different domains, we conduct extensive ex- perimental evaluations to demonstrate the effectiveness and efficiency of the proposed * Corresponding author Email addresses: shaoml@act.buaa.edu.cn (Minglai Shao), sunpy@act.buaa.edu.cn (Peiyuan Sun), lijx@act.buaa.edu.cn (Jianxin Li), qyan@msu.edu (Qiben Yan), fengzr@act.buaa.edu.cn (Zhirui Feng) Preprint submitted to Neurocomputing April 17, 2020