Sparse Topology Identification for Point Process Networks

Abstract

Increasingly in the `big data' era, point process network data is appearing e.g. in social media but also genomics, high frequency finance and neurosicence. And the most basic problem is that of identifying network structure. As with all network system identification problems this is enabled by the use of sparsity. Here, for the first time, we develop a vector l0 sparsity approach for identification of a network of interacting Hawkes processes. We apply the new method to genomic data on a transcriptional regulatory network in embryonic stem cells and the results are compared with previous work and experimental findings.