FrogWild！图引擎上的快速PageRank近似
FrogWild! -- Fast PageRank Approximations on Graph Engines
摘要：
我们提出了FrogWild，一种新的算法快速近似高PageRank顶点，面向降低网络成本的运行传统的PageRank算法。我们的算法可以被看作是一个量化版本的功率迭代，执行多个并行的随机游走在一个有向图。一个重要的创新是，我们引入了一个修改的GraphLab框架，只有部分镜像顶点。这种部分同步极大地减少了传统PageRank算法产生的网络流量，从而大大降低了PageRank的每次迭代成本。另一方面，这种部分同步也会在用于估计PageRank的随机游走之间创建依赖关系。我们的主要理论创新是分析的相关性引入的部分同步过程和建立我们的近似接近真实的PageRank向量的约束。我们在GraphLab中实现了我们的算法，并将其与默认的PageRank实现进行了比较。我们证明了我们的算法非常快，在Twitter图上执行每次迭代不到一秒，并且与标准GraphLab PageRank实现相比，速度可以快7倍。
英文摘要：
We propose FrogWild, a novel algorithm for fast approximation of high PageRank vertices, geared towards reducing network costs of running traditional PageRank algorithms. Our algorithm can be seen as a quantized version of power iteration that performs multiple parallel random walks over a directed graph. One important innovation is that we introduce a modification to the GraphLab framework that only partially synchronizes mirror vertices. This partial synchronization vastly reduces the network traffic generated by traditional PageRank algorithms, thus greatly reducing the per-iteration cost of PageRank. On the other hand, this partial synchronization also creates dependencies between the random walks used to estimate PageRank. Our main theoretical innovation is the analysis of the correlations introduced by this partial synchronization process and a bound establishing that our approximation is close to the true PageRank vector. We implement our algorithm in GraphLab and compare it against the default PageRank implementation. We show that our algorithm is very fast, performing each iteration in less than one second on the Twitter graph and can be up to 7x faster compared to the standard GraphLab PageRank implementation.
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