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Lookup NU author(s): Professor Marcus Kaiser
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Complex networks from domains like Biology or Sociology are present in many e-Science data sets. Dealing with networks can often form a workflow bottleneck as several related algorithms are computationally hard. One example is detecting characteristic patterns or "network motifs" - a problem involving subgraph mining and graph isomorphism. This paper provides a review and runtime comparison of current motif detection algorithms in the field. We present the strategies and the corresponding algorithms in pseudo-code yielding a framework for comparison. We categorize the algorithms outlining the main differences and advantages of each strategy. We finally implement all strategies in a common platform to allow a fair and objective efficiency comparison using a set of benchmark networks.We hope to inform the choice of strategy and critically discuss future improvements in motif detection. © 2009 IEEE.
Author(s): Ribeiro P, Silva F, Kaiser M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 5th IEEE International Conference on e-Science
Year of Conference: 2009
Pages: 80-87
Publisher: IEEE
URL: http://dx.doi.org/10.1109/e-Science.2009.20
DOI: 10.1109/e-Science.2009.20
Library holdings: Search Newcastle University Library for this item
ISBN: 9780769538778
