Using complex networks towards information retrieval and diagnostics in multidimensional imaging.

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks.

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.