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1. 发明授权

US08856047B2 Fast personalized page rank on map reduce 有权
标题翻译：快速个性化页面排名在地图上减少
公开(公告)号：US08856047B2
公开(公告)日：2014-10-07
申请号：US13164788
申请日：2011-06-21
申请人： Kaushik Chakrabarti , Dong Xin , Bahman Bahmani
发明人： Kaushik Chakrabarti , Dong Xin , Bahman Bahmani
IPC分类号： G06F17/10 , G06F17/16 , G06F17/17 , G06F17/30
CPC分类号： G06F17/30864
摘要： A personalized page rank computation system is described herein that provides a fast MapReduce method for Monte Carlo approximation of personalized PageRank vectors of all the nodes in a graph. The method presented is both faster and less computationally intensive than existing methods, allowing a broader scope of problems to be solved by existing computing hardware. The system adopts the Monte Carlo approach and provides a method to compute single random walks of a given length for all nodes in a graph that it is superior in terms of the number of map-reduce iterations among a broad class of methods. The resulting solution reduces the I/O cost and outperforms the state-of-the-art FPPR approximation methods, in terms of efficiency and approximation error. Thus, the system can very efficiently perform single random walks of a given length starting at each node in the graph and can very efficiently approximate all the personalized PageRank vectors.
摘要翻译：本文描述了一种个性化页面排名计算系统，其为图中所有节点的个性化PageRank向量的Monte Carlo近似提供了快速的MapReduce方法。所提出的方法比现有方法更快，计算量更少，允许现有计算硬件解决更广泛的问题。该系统采用蒙特卡罗方法，并提供了一种方法，用于计算图中所有节点的给定长度的单个随机散列，该方法在广泛类方法中的映射减少迭代次数方面是优越的。所产生的解决方案在效率和近似误差方面降低了I / O成本，并且优于现有技术的FPPR近似方法。因此，系统可以非常有效地执行从图中的每个节点开始的给定长度的单个随机游走，并且可以非常有效地接近所有个性化PageRank向量。

2. 发明申请

US20120330864A1 FAST PERSONALIZED PAGE RANK ON MAP REDUCE 有权
标题翻译：快速个性化排序在地图减少
公开(公告)号：US20120330864A1
公开(公告)日：2012-12-27
申请号：US13164788
申请日：2011-06-21
申请人： Kaushik Chakrabarti , Dong Xin , Bahman Bahmani
发明人： Kaushik Chakrabarti , Dong Xin , Bahman Bahmani
IPC分类号： G06N3/12
CPC分类号： G06F17/30864
摘要： A personalized page rank computation system is described herein that provides a fast MapReduce method for Monte Carlo approximation of personalized PageRank vectors of all the nodes in a graph. The method presented is both faster and less computationally intensive than existing methods, allowing a broader scope of problems to be solved by existing computing hardware. The system adopts the Monte Carlo approach and provides a method to compute single random walks of a given length for all nodes in a graph that it is superior in terms of the number of map-reduce iterations among a broad class of methods. The resulting solution reduces the I/O cost and outperforms the state-of-the-art FPPR approximation methods, in terms of efficiency and approximation error. Thus, the system can very efficiently perform single random walks of a given length starting at each node in the graph and can very efficiently approximate all the personalized PageRank vectors.
摘要翻译：本文描述了一种个性化页面排名计算系统，其为图中所有节点的个性化PageRank向量的Monte Carlo近似提供了快速的MapReduce方法。所提出的方法比现有方法更快，计算量更少，允许现有计算硬件解决更广泛的问题。该系统采用蒙特卡罗方法，并提供了一种方法，用于计算图中所有节点的给定长度的单个随机散列，该方法在广泛类方法中的映射减少迭代次数方面是优越的。所产生的解决方案在效率和近似误差方面降低了I / O成本，并且优于现有技术的FPPR近似方法。因此，系统可以非常有效地执行从图中的每个节点开始的给定长度的单个随机游走，并且可以非常有效地接近所有个性化PageRank向量。

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