A Bisubmodular Approach to Event Detection and Prediction in Multivariate Social Graphs

Abstract

A burst event on a social graph is usually framed as an anomalous and unexpected pattern that is characterized as a compact or correlated subset of affected vertices, which is a subgraph. Subgraph detection becomes a serious problem when social graphs involve multiple attributes (i.e., multivariate graph). Most existing methods are not capable of handling the feature selection and subgraph detection problems simultaneously on the multivariate graph. In this article, we propose multivariate anomalous subgraph scanning (MASS), a generic model that detects anomalous events on the multivariate social graph. First, we reformulate the traditional nonparametric statistics as a new statistical objective function that simultaneously measures the significance of a vertices subset and an attributes subset to generate an indicator of ongoing or upcoming events. Then, we reformulate the objective function as the difference between two bisubmodular functions and approximate it with a bisubmodular objective function, which can be optimized in linear time, with an analysis of its theoretical properties. We demonstrate the performance of our proposed method using two burst event detection and prediction tasks from the real world.