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Iterative graph computations are a key component in many big data applications. In my work, I have developed new frameworks to support efficient implementation of iterative graph computations, new distributed systems for analyzing dynamic graphs, and new algorithms for fast approximate computation over graphs that depend on time or on some parameters. In this talk, I focus on one example: the algorithmic challenge of efficient edge-weight personalization for PageRank.
I will first introduce two different ways to personalize PageRank: node weight personalization and edge weight personalization. Node weight personalization changes the teleport probabilities and edge weight personalization changes the transition probabilities in a random surfer model. While there exists many efficient methods for node weight personalization, fast edge weight personalization has been an open problem over a decade.
I will then describe the first fast method for computing PageRank on general graphs when the edge weights are personalized. Based on model reduction, this method is nearly five orders of magnitude faster than the standard approach for an example learning-to-rank application. This speed improvement enables interactive computation of a class of ranking results that previously could only be computed offline.