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中文摘要：
P-Rank是SimRank的扩展形式,也是一种相似度度量方法,被用来计算网络中任意两个结点的相似性。不同于SimRank只考虑结点的入度信息,P-Rank还加入了结点的出度信息,从而更加客观准确地评价结点间的相似程度。随着大数据时代的到来,P-Rank需要处理的数据日益增大。使用MapReduce等分布式模型实现大规模P-Rank迭代计算的方法,本质上是一种同步迭代方法,不可避免地具有同步迭代方法的缺点：迭代时间（尤其是迭代过程中处理器等待的时间）长,计算速度慢,因此效率低下。为了解决这一问题,采用了一种迭代计算方法--异步累积更新算法。这个算法实现了异步计算,减少了计算过程处理器结点的等待时间,提高了计算速度,节省了时间开销。从异步的角度实现了P-Rank算法,将异步累积更新算法应用在了P-Rank上,并进行了对比实验。实验结果表明该算法有效地提高了计算收敛速度。
英文摘要：
P-Rank enriches the traditional similarity measure, SimRank. It is also a method to measure the similarity between two objects in graph model. Different from SimRank which only considers the in-link information, P-Rank also takes the out-link information into consideration. Consequently, P-Rank could effectively and comprehensively measure“how similar two nodes are”. P-Rank is applied widely in graph mining. With the arrival of big-data era, the data scale which P-Rank processes is increasing. The existing methods which implement P-Rank, such as the MapReduce model, are essentially synchronous iterative methods. These methods have some shortcomings in common： the iterative time, especially the waiting time of processors during iterative computing, is long, thus leading to very low efficiency. To solve this problem, this paper uses a new iterative method-the Asynchronous Accumulative Update method. Different from the traditional synchronous methods, this method successfully implementes asynchronous computations and as a result reduces the waiting time of processors during computing. This paper implements P-Rank using the asynchronous accumulative update method, and the experiment results indicate that this method can effectively improve the computation speed.
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