title: Efficient and scalable graph similarity joins in MapReduce.
abstract: 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 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 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.
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Yifan
authors.initials: Y
authors.lastname: Chen
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Xiang
authors.identifier: 0000-0001-6339-0219
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Nagoya University, Nagoya, Japan.
authors.forename: Chuan
authors.identifier: 0000-0001-7239-5134
authors.initials: C
authors.lastname: Xiao
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Weiming
authors.initials: W
authors.lastname: Zhang
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Jiuyang
authors.initials: J
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D012984:Software