NDLI: SAHAD: Subgraph Analysis in Massive Networks Using Hadoop

Content Provider	IEEE Xplore Digital Library
Author	Zhao Zhao Guanying Wang Butt, A.R. Khan, M. Kumar, V.S.A. Marathe, M.V.
Copyright Year	2012
Abstract	Relational sub graph analysis, e.g. finding labeled sub graphs in a network, which are isomorphic to a template, is a key problem in many graph related applications. It is computationally challenging for large networks and complex templates. In this paper, we develop SAHAD, an algorithm for relational sub graph analysis using Hadoop, in which the sub graph is in the form of a tree. SAHAD is able to solve a variety of problems closely related with sub graph isomorphism, including counting labeled/unlabeled sub graphs, finding supervised motifs, and computing graph let frequency distribution. We prove that the worst case work complexity for SAHAD is asymptotically very close to that of the best sequential algorithm. On a mid-size cluster with about 40 compute nodes, SAHAD scales to networks with up to 9 million nodes and a quarter billion edges, and templates with up to 12 nodes. To the best of our knowledge, SAHAD is the first such Hadoop based subgraph/subtree analysis algorithm, and performs significantly better than prior approaches for very large graphs and templates. Another unique aspect is that SAHAD is also amenable to running quite easily on Amazon EC2, without needs for any system level optimization.
Starting Page	390
Ending Page	401
File Size	979463
Page Count	12
File Format	PDF
ISBN	9781467309752
ISSN	15302075
DOI	10.1109/IPDPS.2012.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Partitioning algorithms Algorithm design and analysis Encoding Heuristic algorithms Color Complexity theory Hadoop subgraph isomorphism frequent subgraph motif graphlet frequency distribution MapReduce
Content Type	Text
Resource Type	Article

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