Engineering High-Performance Community Detection Heuristics for Massive Graphs

The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering)...

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Published in:Proceedings of the International Conference on Parallel Processing pp. 180 - 189
Main Authors: Staudt, Christian L., Meyerhenke, Henning
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2013
Subjects:
Algorithm design and analysis
Benchmark testing
Clustering algorithms
Communities
Community detection
graph clustering
Graphics processing units
high-performance network analysis
Linear programming
parallel algorithm engineering
Parallel processing
ISSN:0190-3918
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Abstract The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism is often necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible clustering algorithm framework with shared-memory parallelism. Within this framework we implement our parallel variations of known sequential algorithms and combine them by an ensemble approach. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. The processing rate of our fastest algorithm exceeds 10M edges/second for many large graphs, making it suitable for massive data streams. Moreover, the strongest algorithm we developed yields a very good tradeoff between quality and speed.
AbstractList The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, high-performance analytics algorithms and software tools are necessary. One common graph analytics kernel is community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism is often necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible clustering algorithm framework with shared-memory parallelism. Within this framework we implement our parallel variations of known sequential algorithms and combine them by an ensemble approach. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. The processing rate of our fastest algorithm exceeds 10M edges/second for many large graphs, making it suitable for massive data streams. Moreover, the strongest algorithm we developed yields a very good tradeoff between quality and speed.
Author Meyerhenke, Henning
Staudt, Christian L.
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  givenname: Henning
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  email: meyerhenke@kit.edu
  organization: Inst. of Theor. Inf., KarInstitute of Theoretical Informaticslsruhe Inst. of Technol. (KIT), Karlsruhe, Germany
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Snippet The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information,...
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StartPage 180
SubjectTerms Algorithm design and analysis
Benchmark testing
Clustering algorithms
Communities
Community detection
graph clustering
Graphics processing units
high-performance network analysis
Linear programming
parallel algorithm engineering
Parallel processing
Title Engineering High-Performance Community Detection Heuristics for Massive Graphs
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