Abstract:This paper considers the PageRank centrality computation problem on large graphs. We study asynchronous and distributed algorithms which are operated by aggregating information from local neighbors iteratively. Unlike prior works which rely on stochastic gradient descent (SGD) applied on a least square objective, we derive a stochastic approximation (SA) scheme for solving the PageRank problem by discretizing a linear system of ordinary differential equations. Our approach results in a family of asynchronous a… Show more
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