 |
|
|
|
 |
 |
 |
|
 |
|
 |
|
|
| 王旭丛,李翠平,陈红.大数据下基于异步累积更新的高效P-Rank计算方法.软件学报,2014,25(9):2136-2148 |
| 大数据下基于异步累积更新的高效P-Rank计算方法 |
| High-Efficiency P-Rank Computation Through Asynchronous Accumulative Updates in Big Data Environment |
| 投稿时间：2014-01-24 修订日期：2014-04-30 |
| DOI：10.13328/j.cnki.jos.004637 |
| 中文关键词: 异步累积更新 大数据 相似度 P-Rank 大规模计算 |
| 英文关键词:asynchronous accumulative update big data similarity P-Rank large-scale computation |
| 基金项目:国家自然科学基金(61272137, 61033010, 61202114); 国家高技术研究发展计划(863)(2014AA015204); 国家基础研究发展计划(973)(2012CB316205); 国家社会科学基金(12&ZD220); 中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)(10XNI018) |
|
| 摘要点击次数: 4291 |
| 全文下载次数: 2843 |
| 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. |
|
HTML 下载PDF全文 查看/发表评论 下载PDF阅读器 |
|
|
|
|
|
|
 |
|
|
|
|
 |
|
 |
|
 |
|
