Inexact subgraph isomorphism in MapReduce

Inexact subgraph matching based on type-isomorphism was introduced by Berry et al. [J. Berry, B. Hendrickson, S. Kahan, P. Konecny, Software and algorithms for graph queries on multithreaded architectures, in: Proc. IEEE International Parallel and Distributed Computing Symposium, IEEE, 2007, pp. 1–1...

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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 73; no. 2; pp. 164 - 175
Main Author: Plantenga, Todd
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01.02.2013
Elsevier
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Biological and medical sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects
Graph mining
Information retrieval. Graph
MapReduce
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Pattern match
Software
Subgraph isomorphism
Theoretical computing
Subgraph isomorphism
Pattern match
MapReduce
Graph mining
Isomorphic graph
Parallel algorithm
Edge(graph)
Very large databases
Social network
Graph theory
Data mining
Distributed computing
Narrowing(logics)
Multithread
Massive parallelism
Isomorphism
Subgraph
Bioinformatics
Power law
Power
ISSN:0743-7315, 1096-0848
Online Access:Get full text
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Summary:Inexact subgraph matching based on type-isomorphism was introduced by Berry et al. [J. Berry, B. Hendrickson, S. Kahan, P. Konecny, Software and algorithms for graph queries on multithreaded architectures, in: Proc. IEEE International Parallel and Distributed Computing Symposium, IEEE, 2007, pp. 1–14] as a generalization of the exact subgraph matching problem. Enumerating small subgraph patterns in very large graphs is a core problem in the analysis of social networks, bioinformatics data sets, and other applications. This paper describes a MapReduce algorithm for subgraph type-isomorphism matching. The MapReduce computing framework is designed for distributed computing on massive data sets, and the new algorithm leverages MapReduce techniques to enable processing of graphs with billions of vertices. The paper also introduces a new class of walk-level constraints for narrowing the set of matches. Constraints meeting criteria defined in the paper are useful for specifying more precise patterns and for improving algorithm performance. Results are provided on a variety of graphs, with size ranging up to billions of vertices and edges, including graphs that follow a power law degree distribution. ► A MapReduce implementation of inexact subgraph isomorphism is described. ► Experiments show scalability on graphs with billions of vertices and edges. ► A general framework for walk-level constraints is provided. ► Examples of constraints include rectangle and 4-clique subgraph patterns.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2012.10.005