DISTRIBUTED TOPOLOGY IDENTIFICATION FOR POINT PROCESS DYNAMIC NETWORKS

Recently, the availability of high-dimensional point process data in a growing number of application areas is driving the demand for analysis tools for such data. An extremely challenging yet important problem is the inference of causal relationships from network data or network topology identification. This problem has received little attention in the literature until very recently. Here we develop, perhaps for the first time, a distributed optimization algorithm for large-scale dynamic networks of interacting Hawkes processes. Genomic data are analyzed to construct a transcriptional regulatory network in embryonic stem cells.