双向PageRank估计：从平均情况到最坏情况
Bidirectional PageRank Estimation: From Average-Case to Worst-Case
摘要：
我们提出了一种新的算法，用于估计的个性化PageRank（PPR）之间的源和目标节点的无向图，次线性运行时间保证在最坏情况下的源和目标节点的选择。我们的工作建立在最近的工作线上的双向估计PPR，获得次线性运行时间的保证，但在平均情况下，目标节点的均匀随机选择。至关重要的是，我们展示了如何利用无向网络上随机游走的可逆性将平均情况转换为最坏情况的保证。过去的双向方法将正向随机游走与反向本地推送相结合，而我们的算法将正向本地推送与反向随机游走相结合。我们还讨论了如何修改我们的方法，以估计任何长度分布的随机行走概率，从而获得快速算法估计一般图扩散，包括热核，无向网络。
英文摘要：
We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how the reversibility of random walks on undirected networks can be exploited to convert average-case to worst-case guarantees. While past bidirectional methods combine forward random walks with reverse local pushes, our algorithm combines forward local pushes with reverse random walks. We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
