The introduction of randomization in the design and analysis of algorithms for matrix computations (such as Principal Components Analysis and the related Singular Value Decomposition (SVD)) over the last decade made them feasible for matrices that arise in the context of the large networks. In this talk we will discuss these developments, their extensions, and their applications to network analysis.
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