Practising Scalable Graph Similarity Joins in MapReduce
Yifan Chen, Xiang Hui Zhao, Bin Ge, Chuan Xiao, Chi‐Hung Chi · 2014
Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity join due to its wide applications for multiple purposes, including data cleaning, near duplicate detection, etc. 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 propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out non-promising 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. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results.