Dynamic Shaker Detection from Evolving Entities

Finding the most influential entities as well as conducting causality analysis is an important topic in economics, healthcare, sensor networks, etc. One famous example in economics is the bankruptcy of Lehman Brothers that triggered the 2008 global financial crisis. In recent years, some works were proposed to infer the causal relationships among several entities, and subsequently find the most influential ones (i.e., shakers). However, most of the previous works assume that the causal relationships and the shakers are static. In other words, they are assumed to be stable over time. This assumption may not necessarily be true, especially when we study volatile entities or long-term time series. In this paper, we propose a dynamic model called “DShaker” to capture the evolving causal relationships and dynamic shakers. The intuition is to model the causality propagation into a graph called “dynamic cascading graph”. We then find the optimal cascading graphs, by maximizing their likelihoods in a non-convex multi-objective optimization formulation. We solve it by mapping into a trace norm minimization problem. Experiments included three datasets in social sciences. The proposed method can effectively capture those entities with increasing impacts, while existing methods missed most of them. For example, in the experiment of studying the banks’ statistics from 1998 to 2007 (before the financial crisis), the proposed method successfully captures Lehman Brothers as one of the most precarious banks in subprime loans.