SAHAD: Subgraph Analysis in Massive Networks Using Hadoop

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 relation...

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Veröffentlicht in:2012 IEEE 26th International Parallel and Distributed Processing Symposium S. 390 - 401
Hauptverfasser: Zhao Zhao, Guanying Wang, Butt, A. R., Khan, M., Kumar, V. S. A., Marathe, M. V.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2012
Schlagworte:
Algorithm design and analysis
Color
Complexity theory
Encoding
frequent subgraph
graphlet frequency distribution
Hadoop
Heuristic algorithms
Image color analysis
MapReduce
motif
Partitioning algorithms
subgraph isomorphism
ISBN:1467309753, 9781467309752
ISSN:1530-2075
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Zusammenfassung: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.
ISBN:1467309753
9781467309752
ISSN:1530-2075
DOI:10.1109/IPDPS.2012.44