Inferring Directed Static Networks of Influence from Undirected Temporal Networks

A temporal network consists of a time series of interaction events, each of which is defined by a triplet composed of the indices of two nodes and the time of the event. Mapping a temporal network to a more tractable static network is often useful. A mapping method was recently proposed on the basis of the so-called transfer entropy (G. V. Steeg and A. Galstyan, in Proc. the 21st Int. Conf. WWW, p.509, 2012). In the proposed method, one generates the directed network of influence in which a directed link represents the causal relationship between activity patterns at two nodes. However, the significance of the inferred links and the sensitivity of results to the parameter values are still unclear. We propose a bootstrap sampling method to statistically configure the directed network of influence. We apply our method to the face-to-face interaction logs between office workers in Japanese companies.