In the age of network sciences and machine learning, efficient algorithms are now in higher demand more than ever before. Big Data fundamentally challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. Using several basic tasks in network analysis, social influence modeling, machine learning, and optimization as examples - in this talk - I will highlight a family of fundamental algorithmic techniques for designing provably-good scalable algorithms.
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