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In view of the analysis of the structural characteristics of the multilayer network (species-sharing species-other species) has been complete, however, there is still a lack of a unified operation that can quickly obtain the corresponding characteristics of the multilayer network and explore Mesoscale structure of the network (motif). To solve this insufficiency, ‘ILSM’ was designed for supporting calculating such metrics of multilayer networks by functions of R package.
The visualization of an example multilayer interaction network with three parties of nodes and two interaction layers (a, two common forms in literature) and interconnection structures (b, interconnection pattern, c, interconnection motif, d, versatility). Different colors of nodes indicate different parties of species, and the dashed lines indicate the shared species. (a) The shared species in the left panel were indicated by dashed links, and those in the right panel have links from both layers.
You can install the development version of ILSM from GitHub:
devtools::install_github("WeichengSun/ILSM")
In multilayer network, 48 types of motifs of nodes ranging from 3 to 6 are shown. The green squares, cyan circles, and blue squares represent the first, second, and third groups of nodes of the network, respectively. The graph in the first row represents only one node in the middle group with increasing complexity, similarly, the graph in the second to fourth rows represents a motif with two nodes in the middle group, and finally the motif in the fifth row represents a motif with three nodes node in the middle group, which also tends to be the most complex. The numbers from 1 to 70 in the cyan circle represent the roles in which the 70 nodes are located.
This is a basic example which shows you how to solve a common problem:
library(ILSM)
## generate a network
set.seed(12)
N <- build_net(11,15,16,0.2)
## calculate the frequency of motifs
icmotif_count(N)
## measure the roles of connector node
icmotif_role(N)
## Five interconnection pattern
coid(N)
cois(N)
poc(N)
pc(N)
hc(N)
## examine node versatility
node_cv(N)The code is released under the MIT license (see LICENSE file).
Simmons, B. I., Sweering, M. J., Schillinger, M., Dicks, L. V., Sutherland, W. J., & Di Clemente, R. (2019). bmotif: A package for motif analyses of bipartite networks. Methods in Ecology and Evolution, 10(5), 695-701.
Mora, B.B., Cirtwill, A.R. and Stouffer, D.B., 2018. pymfinder: a tool for the motif analysis of binary and quantitative complex networks. bioRxiv, 364703.
Domínguez-García, V., & Kéfi, S. (2024). The structure and robustness of ecological networks with two interaction types. PLOS Computational Biology, 20(1), e1011770.
Sauve, A. M., Thébault, E., Pocock, M. J., & Fontaine, C. (2016). How plants connect pollination and herbivory networks and their contribution to community stability. Ecology, 97(4), 908-917.
Pilosof, S., Porter, M. A., Pascual, M., & Kéfi, S. (2017). The multilayer nature of ecological networks. Nature Ecology & Evolution, 1(4), 0101.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.