Efficient and Scalable Graph Similarity Joins in MapReduce

Yifan Chen, Xiang Zhao, Chuan Xiao, Weiming Zhang, Jiuyang Tang
2014 The Scientific World Journal  
Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we proposeMGSJoin, a scalable algorithm following
more » ... filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results.
doi:10.1155/2014/749028 pmid:25121135 pmcid:PMC4121100 fatcat:w66vd5lfjbg5tfdmimlnsji4qu