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Parallel algorithms for flexible pattern matching on big graphs
الگوریتم های موازی برای تطبیق الگوی انعطاف پذیر در نمودارهای بزرگ-2018 Strong simulation is a state-of-the-art approximate scheme in graph pattern matching. This
scheme always finds high-quality results compared to other schemes. However, as the Web
and social networks are increasingly used in human lives, the scale of the data grows ex
tremely large. As a result, such big graphs are often stored in the distributed environment,
in order to be managed efficiently. Unfortunately, current distributed algorithm for strong
simulation is not efficient and cannot be applied to real applications. In this paper, we
propose efficient parallel algorithms for strong simulation in the distributed setting. The
contribution includes (1) We convert the calculation of strong simulation to calculating
a relative small set of partial results for each partition of pattern suitable for distributed
system. (2) We develop a method to reduce data shipment and time complexity of local
computation in the distributed setting. (3) We split the distributed calculation of strong
simulation into an offline redistribution algorithm and an online matching algorithm. The
major data shipment is involved in the offline algorithm, while the online algorithm is
highly parallel and much more efficient than current algorithms. (4) By experiments on
both real and synthetic data, we verify the efficiency of our distributed algorithms and the
effectiveness of our scheme without large intermediate results.
Keywords: Graph query ، Graph simulation ، Distributed algorithms ، Strong simulation |
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