2009 Fifth IEEE International Conference on e-Science (2009)
Oxford, United Kingdom
Dec. 9, 2009 to Dec. 11, 2009
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.
Network Motifs, Graph Mining, Algorithms, Complex Networks
F. Silva, P. Ribeiro and M. Kaiser, "Strategies for Network Motifs Discovery," 2009 Fifth IEEE International Conference on e-Science(ESCIENCE), Oxford, United Kingdom, 2009, pp. 80-87.