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Evaluation of subgraph searching algorithms detecting network motif in biological networks

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Abstract

Despite several algorithms for searching subgraphs in motif detection presented in the literature, no effort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumerate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size motif detection, and ESU has its own advantage in small-size motif detection.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Jialu Hu, Lin Gao & Guimin Qin

  2. Software School, Xidian University, Xi’an, 710071, China

    Guimin Qin

Authors
  1. Jialu Hu
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  2. Lin Gao
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  3. Guimin Qin
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Correspondence to Lin Gao.

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Hu, J., Gao, L. & Qin, G. Evaluation of subgraph searching algorithms detecting network motif in biological networks. Front. Comput. Sci. China 3, 412–416 (2009). https://doi.org/10.1007/s11704-009-0045-z

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  • DOI: https://doi.org/10.1007/s11704-009-0045-z

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