bmotif: counting motifs in bipartite networks
bmotif is software to count occurrences of motifs in bipartite networks, as well as the number of times each node appears in each unique position within motifs.
bmotif considers all 44 unique bipartite motifs up to six nodes and all 148 unique positions within these motifs. It is available in R, MATLAB and Python.
bmotif was originally developed to analyse bipartite species interaction networks in ecology but its methods are general and can be applied to any bipartite graph.
Motif and motif position dictionary
The motifs corresponding to each motif ID and the positions corresponding to each motif position ID can be found in Simmons, B. I., Sweering, M. J. M., Schillinger, M., Dicks, L. V., Sutherland W. J., Di Clemente, R. bmotif: a package for motif analyses of bipartite networks. Methods in Ecology and Evolution (accepted). These were defined following Baker et al (2015) Appendix 1 Figure A27.
- Main.m: Shows how to use the two main functions on a simple example network
- motifs.m: Counts the number of times motifs occur in a network
- Position_motifs.m: Counts the number of times nodes occur in unique positions within motifs
- Check_mot.m: Internal function for checking that the input arguments for motifs.m are valid; called by motifs.m
- Check_pos.m: Internal function for checking that the input arguments for Position_motifs.m are valid; called by Position_motifs.m
- tensor_make.m: Internal function for making tensor; called by motifs.m and Position_motifs.m
- tensorR.m: Internal function for calculating the tensor product of arrays; called by motifs.m and Position_motifs.m
The code is released under the MIT license (see LICENSE file).
If you use the package in your work, please cite: Simmons, B. I., Sweering, M. J. M., Schillinger, M., Dicks, L. V., Sutherland W. J., Di Clemente, R. bmotif: a package for motif analyses of bipartite networks. Methods in Ecology and Evolution (accepted)
Baker, N.J., Kaartinen, R., Roslin, T. and Stouffer, D.B., 2015. Species’ roles in food webs show fidelity across a highly variable oak forest. Ecography, 38(2), pp.130-139.