The Value of Temporally Richer Data for Learning of Influence Networks

Abstract

Consumers adopting a new product; an epidemic spreading across a population; a sovereign debt crisis hitting several countries; a cellular process during which the expression of a gene affects the expression of other genes; an article trending in the blogosphere, a topic trending on an online social network, computer malware spreading across a network; all of these are temporal processes governed by local interactions of networked entities, which influence one another. Due to the increasing capability of data acquisition technologies, rich data on the outcomes of such processes are oftentimes available (possibly with time stamps), yet the underlying network of local interactions is hidden. In this work, we infer who influences whom in a network of interacting entities based on data of their actions/ decisions, and quantify the gain of learning based on sequences of actions versus sets of actions. We answer the following question: how much faster can we learn influences with access to increasingly informative temporal data (sets versus sequences)?