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Topology identification for sparse dynamic point process networks

The availability of high dimensional data in recent times is driving the development of more sophisticated statistical tools for analysis. This is especially true where the data are point processes. An extremely challenging yet important problem is the identification of causal relationships from network data or topology identification in networks involving point processes. This problem has received little attention in the literature until very recently. Here we discuss topology identification in a dynamic network of interacting Hawkes processes. Genomic data are analyzed to construct a transcriptional regulatory network in embryonic stem cells.