The Community for Technology Leaders
2012 IEEE 8th International Conference on E-Science (2009)
Oxford, United Kingdom
Dec. 9, 2009 to Dec. 11, 2009
ISBN: 978-0-7695-3877-8
pp: 80-87
ABSTRACT
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.
INDEX TERMS
Network Motifs, Graph Mining, Algorithms, Complex Networks
CITATION
Fernando Silva, Pedro Ribeiro, Marcus Kaiser, "Strategies for Network Motifs Discovery", 2012 IEEE 8th International Conference on E-Science, vol. 00, no. , pp. 80-87, 2009, doi:10.1109/e-Science.2009.20
82 ms
(Ver 3.3 (11022016))