Use of transfer entropy to infer relationships from behavior

Abstract

This paper discusses the use of transfer entropy to infer relationships among entities. This is useful when one wants to understand relationships among entities but can only observe their behavior, but not direct interactions with one another. This is the kind of environment prevelant in network monitoring, where one can observe behavior coming into and leaving a network from many different hosts, but cannot directly observe which hosts are related to one another. In this paper, we show that networks of individuals inferred using the transfer entropy of Wikipedia editing behavior predicts observed "ground truth" social networks. At low levels of recall, transfer entropy can extract these social networks with a precision approximately 20 times higher than would be expected by chance. We'll discuss the algorithm, the data set, and various parameter considerations when attempting to apply this algorithm to a data set.