Scalable subgraph enumeration in MapReduce: a cost-oriented approach

Scalable subgraph enumeration in MapReduce: a cost-oriented approach Subgraph enumeration, which aims to find all the subgraphs of a large data graph that are isomorphic to a given pattern graph, is a fundamental graph problem with a wide range of applications. However, existing sequential algorithms for subgraph enumeration fall short in handling large graphs due to the involvement of computationally intensive subgraph isomorphism operations. Thus, some recent researches focus on solving the problem using MapReduce. Nevertheless, exiting MapReduce approaches are not scalable to handle very large graphs since they either produce a huge number of partial results or consume a large amount of memory. Motivated by this, in this paper, we propose a new algorithm $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig $$\mathsf {Join}$$ Join based on a left-deep-join framework in MapReduce, in which the basic join unit is a $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig (an edge or two incident edges of a node). We show that in the Erdös–Rényi random graph model, $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig $$\mathsf {Join}$$ Join is instance optimal in the left-deep-join framework under reasonable assumptions, and we devise an algorithm to compute the optimal join plan. We further discuss how our approach can be adapted to handle the power-law random graph model. Three optimization strategies are explored to improve our algorithm. Ultimately, by aggregating equivalent nodes into a compressed node, we construct the compressed graph, upon which the subgraph enumeration is further improved. We conduct extensive performance studies in several real graphs, one of which contains billions of edges. Our approach significantly outperforms existing solutions in all tests. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Scalable subgraph enumeration in MapReduce: a cost-oriented approach

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-017-0459-4
Publisher site
See Article on Publisher Site

Abstract

Subgraph enumeration, which aims to find all the subgraphs of a large data graph that are isomorphic to a given pattern graph, is a fundamental graph problem with a wide range of applications. However, existing sequential algorithms for subgraph enumeration fall short in handling large graphs due to the involvement of computationally intensive subgraph isomorphism operations. Thus, some recent researches focus on solving the problem using MapReduce. Nevertheless, exiting MapReduce approaches are not scalable to handle very large graphs since they either produce a huge number of partial results or consume a large amount of memory. Motivated by this, in this paper, we propose a new algorithm $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig $$\mathsf {Join}$$ Join based on a left-deep-join framework in MapReduce, in which the basic join unit is a $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig (an edge or two incident edges of a node). We show that in the Erdös–Rényi random graph model, $$\mathsf {Twin}$$ Twin $$\mathsf {Twig}$$ Twig $$\mathsf {Join}$$ Join is instance optimal in the left-deep-join framework under reasonable assumptions, and we devise an algorithm to compute the optimal join plan. We further discuss how our approach can be adapted to handle the power-law random graph model. Three optimization strategies are explored to improve our algorithm. Ultimately, by aggregating equivalent nodes into a compressed node, we construct the compressed graph, upon which the subgraph enumeration is further improved. We conduct extensive performance studies in several real graphs, one of which contains billions of edges. Our approach significantly outperforms existing solutions in all tests.

Journal

The VLDB JournalSpringer Journals

Published: Feb 21, 2017

References

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