TPA：十亿级图上带重启的近似随机游动的快速、可扩展、精确算法
TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
摘要：
给定一个大型图，我们如何快速准确地确定节点之间的相似性？重新开始随机游走（RWR）是用于此目的的流行措施，并且已经在许多数据采掘应用中被利用，包括排名、异常检测、链接预测和社区发现。然而，用于计算精确RWR的先前方法需要过高的存储大小和计算成本，并且通过计算近似RWR来避免这种成本的替代方法具有有限的精度。在本文中，我们提出了TPA，一个快速，可扩展的，高精度的方法计算近似RWR的大型图。TPA利用了RWR中的两个重要属性：1）由于许多真实世界图的分块结构，靠近种子节点的节点可能会在后续步骤中被重新访问，以及2）远离种子节点的节点的RWR分数与其PageRank分数成比例。基于这两个性质，TPA将近似RWR问题分解为两个子问题，分别称为近邻近似和陌生人近似。在邻居近似中，TPA基于种子的几个早期步骤的分数来估计靠近种子的节点的RWR分数。在陌生人近似中，TPA使用其PageRank估计远离种子的节点的RWR分数。陌生人近似和邻居近似分别在预处理阶段和在线阶段进行。通过大量的实验，我们表明，TPA需要高达3.5倍的时间，高达40倍的存储空间比其他国家的最先进的方法的预处理阶段。在在线阶段，TPA计算近似RWR的速度比现有方法快30倍，同时保持高精度。
英文摘要：
Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exploited in numerous data mining applications including ranking, anomaly detection, link prediction, and community detection. However, previous methods for computing exact RWR require prohibitive storage sizes and computational costs, and alternative methods which avoid such costs by computing approximate RWR have limited accuracy. In this paper, we propose TPA, a fast, scalable, and highly accurate method for computing approximate RWR on large graphs. TPA exploits two important properties in RWR: 1) nodes close to a seed node are likely to be revisited in following steps due to block-wise structure of many real-world graphs, and 2) RWR scores of nodes which reside far from the seed node are proportional to their PageRank scores. Based on these two properties, TPA divides approximate RWR problem into two subproblems called neighbor approximation and stranger approximation. In the neighbor approximation, TPA estimates RWR scores of nodes close to the seed based on scores of few early steps from the seed. In the stranger approximation, TPA estimates RWR scores for nodes far from the seed using their PageRank. The stranger and neighbor approximations are conducted in the preprocessing phase and the online phase, respectively. Through extensive experiments, we show that TPA requires up to 3.5x less time with up to 40x less memory space than other state-of-the-art methods for the preprocessing phase. In the online phase, TPA computes approximate RWR up to 30x faster than existing methods while maintaining high accuracy.
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。
