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Graphs are widely used to model complicated data semantics in many applications in bioinformatics, chemistry, social networks, pattern recognition, etc. A recent trend is to tolerate noise arising from various sources, such as erroneous data entry, and find similarity matches. In this paper, we study the graph similarity join problem that returns pairs of graphs such that their edit distances are no larger than a threshold. Inspired by the -gram idea for string similarity problem, our solutiondoi:10.1109/icde.2012.91 dblp:conf/icde/ZhaoXLW12 fatcat:uder5qe7onhi5ly4etwo4ia5iu