Parallelized Similarity Flooding Algorithm for Processing Large Scale Graph Datasets with MapReduce

被引：0

作者：

Zhang, Jian ^{[1
]}

Yuan, Chunfeng ^{[1
]}

Huang, Yihua ^{[1
]}

机构：

[1] Nanjing Univ, Natl Key Lab Novel Software Technol, Dept Comp Sci & Technol, Nanjing 210093, Jiangsu, Peoples R China

来源：

2012 13TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS, AND TECHNOLOGIES (PDCAT 2012) | 2012年

关键词：

similarity flooding algorithm; large-scale graph data; parallelized algorithm; MapReduce;

D O I：

10.1109/PDCAT.2012.109

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Measures of graph similarity have a broad range of applications but involve compute-intensive process. Similarity flooding algorithm is an efficient algorithm for comparing the similarity of graphs of small size and small datasets. However, nowadays more and more large-scale graph applications emerge and existing stand-alone similarity flooding algorithm cannot efficiently conduct the similarity comparison process for large scale graph datasets in acceptable time. This paper presents a parallelized similarity flooding algorithm with MapReduce for large-scale graph datasets. The experimental results demonstrate that the parallelized algorithm achieves significant performance improvement compared to the stand-alone similarity flooding algorithm. Experimental results also reveal that the parallelized algorithm can obtain excellent speedup when the size of cluster increases.

引用

页码：184 / 188

页数：5