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Network analytics in the age of big data : How can we holistically mine big data?
Pržulj, Nataša, 1973- ; Malod-Dognin, Noël
We live in a complex world of interconnected entities. In all areas of human endeavor, from biology to medicine, economics, and climate science, we are flooded with large-scale data sets. These data ...sets describe intricate real-world systems from different and complementary viewpoints, with entities being modeled as nodes and their connections as edges, comprising large networks. These networked data are a new and rich source of domain-specific information, but that information is currently largely hidden within the complicated wiring patterns. Deciphering these patterns is paramount, because computational analyses of large networks are often intractable, so that many questions we ask about the world cannot be answered exactly, even with unlimited computer power and time [M. R. Garey, D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Freeman, New York, 1979).]. Hence, the only hope is to answer these questions approximately (that is, heuristically) and prove how far the approximate answer is from the exact, unknown one, in the worst case. [A. R. Benson et al., Science 353, 163 (2016).] take an important step in that direction by providing a scalable heuristic framework for grouping entities based on their wiring patterns and using the discovered patterns for revealing the higher-order organizational principles of several real-world networked systems.
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