Kavosh: a new algorithm for finding network motifs. Background: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. Results: We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network. Conclusion: The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

References in zbMATH (referenced in 12 articles )

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  1. Komusiewicz, Christian; Sommer, Frank: Enumerating connected induced subgraphs: improved delay and experimental comparison (2021)
  2. Xie, Guangqiang; Chen, Junyu; Li, Yang: Hybrid-order network consensus for distributed multi-agent systems (2021)
  3. Rubert, Diego P.; Araujo, Eloi; Stefanes, Marco A.; Stoye, Jens; Martinez, Fábio V.: Searching and inferring colorful topological motifs in vertex-colored graphs (2020)
  4. Yu, Shuo; Feng, Yufan; Zhang, Da; Bedru, Hayat Dino; Xu, Bo; Xia, Feng: Motif discovery in networks: a survey (2020)
  5. Chen, Yinghan; Chen, Yuguo: An efficient sampling algorithm for network motif detection (2018)
  6. Erciyes, K.: Distributed and sequential algorithms for bioinformatics (2015)
  7. Shahrivari, Saeed; Jalili, Saeed: Distributed discovery of frequent subgraphs of a network using MapReduce (2015)
  8. Ribeiro, Pedro; Silva, Fernando: G-Tries: a data structure for storing and finding subgraphs (2014)
  9. Birmelé, Etienne: Detecting local network motifs (2012)
  10. Ribeiro, Pedro; Silva, Fernando; Lopes, Luís: Parallel discovery of network motifs (2012) ioport
  11. Zweig, Katharina A.: Good versus optimal: why network analytic methods need more systematic evaluation (2011) ioport
  12. Kashani, Zahra Razaghi Moghadam; Ahrabian, Hayedeh; Elahi, Elahe; Nowzari-Dalini, Abbas; Ansari, Elnaz Saberi; Asadi, Sahar; Mohammadi, Shahin; Schreiber, Falk; Masoudi-Nejad, Ali: Kavosh: a new algorithm for finding network motifs (2009) ioport