Converting network-unlike data into complex networks: Problems and prospective

Often network science with complex networks as its basic entity has attracted scientific societies with their diverse practical capacities. Some entities (objects, processes, and data) having their built-in web nature might be considered as networks seamlessly. Contrary, network mapping for Network-Unlike Data (NUD), i.e. images and time series, is extremely complicated and manifold, so that explorers face with a tough problem which converting algorithm they should apply. We put in central focus separating data properties in line with their scale diversity-in distance, time, and nature and suggested a three step algorithm (scale-based one) to map NUD into complex networks. The algorithm was applied to networkalize two types of geographic maps of Olkhon district, Baikal Natural Territory, Irkutsk region, Russian Federation. It was underlined that the algorithm models coarse-graining and area-like linking and forms thoroughly output structures of really complex topologies with intrinsic scale-free and small world properties. In addition to simple examples transformation of NUD into multiplex networks is considered as a promising approach to study more complex systems of the real world. Networkalization concerned challenges in extracting the pertinent information from huge data resources conveyed by a network imprint for each file is also discussed.