Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study

Jul 12, 2020



PageRank is a widely used approach for measuring the importance of a node in a graph. Computing PageRank is a fundamental task in numerous applications including web search, machine learning and recommendation systems. The importance of computing PageRanks in a distributed environment has been recognized due to the rapid growth of the graph size in real world. However, only a few previous works can provide a provable complexity and accuracy for distributed PageRank computation. Given a constant d>0 and a graph of n nodes and under the well-known congested-clique distributed model, the state-of-the-art approach, Radar-Push, uses O(loglogn+logd) communication rounds to approximate the PageRanks within a relative error O(1/log^dn). However, Radar-Push entails as large as O(log^2d+3n) bits of bandwidth (e.g., the communication cost between a pair of nodes per round) in the worst case. In this paper, we provide a new algorithm that uses asymptotically the same communication rounds while significantly improves the bandwidth from O(log^2d+3n) bits to O(dlog^3n) bits. To the best of our knowledge, our distributed PageRank algorithm is the first to achieve o(dlogn) communication rounds with O(dlog^3n) bits of bandwidth in approximating PageRanks with relative error O(1/log^dn) under the congested-clique model.


About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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