  We are flooded with large-scale dynamic networked data. Analyses requiring
exact comparisons between networks are computationally intractable, so new
methodologies are sought. We extend the graphlet-based statistics to directed
networks and demonstrate that they are superior to other measures. We predict a
country's gross domestic product (GDP) solely from its wiring patterns in the
world trade network (WTN) that could inform policy makers on benefits of trade
agreements. Surprisingly, we find that it is not enough for a country to be in
a densely connected core in the WTN to have a high GDP, as was previously
believed. In addition to being in the core, a country must also trade with
peripheral countries, while only being in the core and not trading with
peripheral economies makes a country prone to debt. Furthermore, by tracking
the dynamics of a country's positioning in the WTN over years, we predict
success or failure of an emerging economy. We validate our methodology on
metabolic networks, yielding insights into preservation of enzyme function from
the network wiring patterns rather than sequence data. Overall, our methodology
enables advanced analyses of directed networked data from any area of science,
allowing domain-specific interpretation of a directed network's topology.
