Discovering shakers from evolving entities via cascading graph inference

Abstract

In an interconnected and dynamic world, the evolution of one entity may cause a series of significant value changes for some others. For example, the currency inflation of Thailand caused the currency slump of other Asian countries, which eventually led to the financial crisis of 1997. We call such high impact entities shakers. To discover shakers, we first introduce the concept of a cascading graph to capture the causality relationships among evolving entities over some period of time, and then infer shakers from the graph. In a cascading graph, nodes represent entities and weighted links represent the causality effects. In order to find hidden shakers in such a graph, two scoring functions are proposed, each of which estimates how much the target entity can affect the values of some others. The idea is to artificially inject a significant change on the target entity, and estimate its direct and indirect influence on the others, by following an inference rule under the Markovian assumption. Both scoring functions are proven to be only dependent on the structure of a cascading graph and can be calculated in polynomial time. Experiments included three datasets in social sciences. Without directly applicable previous methods, we modified three graphical models as baselines. The two proposed scoring functions can effectively capture those high impact entities. For example, in the experiment to discover stock market shakers, the proposed models outperform the three baselines by as much as 50% in accuracy with the ground truth obtained from Yahoo! Finance. Copyright 2011 ACM.