ILSM (version 1.1.0.1)

icmotif_count: Count interconnection motifs

Description

Counting the frequencies of interconnection motifs for a tripartite interaction network.

Usage

icmotif_count(network.or.subnet_mat1, subnet_mat2 = NULL, weighted = FALSE)

Value

Return a data.fame of the frequencies (and mean weight) of 48 interconnection motifs. See 'Multi_motif' for the forms.

Arguments

network.or.subnet_mat1

An igraph object or matrix. An "igraph" object with node attribute 'level' or a matrix representing one subnetwork. See details.

subnet_mat2

A matrix representing one subnetwork.

weighted

Logical. Default to FALSE. If TRUE, the arithmetic mean of the subgraph weights is provided for each motif. See details

Details

In this package, a tripartite network contains three groups of nodes (a-nodes,b-nodes,c-nodes) and two subnetworks (P includes the links between a-nodes and b-nodes, Q includes the links between b-nodes and c-nodes). Connector nodes belong to b-nodes.
An interconnection motif is defined to comprise three sets of connected nodes: the connector nodes (belonging to b-nodes), the nodes in one subnetwork (belonging to a-nodes in the P subnetwork), and the nodes in the other subnetwork (belonging to c-nodes in the Q subnetwork). Each motif has maximumly 6 nodes, resulting in a total of 48 distinct motif forms. The algorithm for counting interconnection motifs is designed by extending the fast approach from Simmons et al.(2019), which uses mathematical operations directly on the bi-adjacency matrix. For interconnection motifs in tripartite networks with intra-guild interactions, please see ig_icmotif_count and ig_icmotif_role.

Two types of inputs network.or.subnet_mat1 can be processed:

• An "igraph" object with node attribute 'level' (0 for a-nodes, 1 for b-nodes,2 for c-nodes). If the input is a weighted network, the edge should have a 'weight' attribute.

• Or a matrix representing subnetwork P, and must be input with subnet_mat2 representing subnetwork Q.

If the inputs are two matrices, please make sure the rows of network.or.subnet_mat1 and subnet_mat2 both represent the groups of connector species,i.e, the b-group species. If both matrices have row names, then the function matches row names to produce connector nodes. Otherwise, row numbers are assigned to row names and matched. Within the two matrices (P and Q), columns represents a-group nodes and c-group nodes respectively. Elements in matrices are non-zero values if two nodes are linked with or without weights, and 0 otherwise.

Weighted networks
For weighted tripartite networks, the mean weight of a given motif is provided by averaging the weights of all the cases of a particular motif. The weight of a motif case is the arithmetic mean of the weights of its links, following Mora et al. (2018) and Simmons et al. (2019).

References

Pilosof, S., Porter, M. A., Pascual, M., & KC)fi, S. (2017). The multilayer nature of ecological networks. Nature Ecology & Evolution, 1(4), 0101.

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.

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.

Examples

## generate a random tripartite network
set.seed(12)
Net <- build_toy_net(11,15,16,0.2)
icmotif_count(Net)

## empirical network
data(PPH_Coltparkmeadow)
Net <- PPH_Coltparkmeadow
icmotif_count(Net)
set.seed(13)
library(igraph)
E(Net)$weight<-runif(length(E(Net)),0.1,1)#random weights assigned
icmotif_count(Net, weighted=TRUE)


##input as binary matrices, with row names.
set.seed(12)
md1 <- matrix(sample(c(0,1),8*11,replace=TRUE),8,11)
dimnames(md1) = list(paste0("b",1:8),paste0("c",1:11))
md2 <- matrix(sample(c(0,1),10*12,replace=TRUE),10,12)
dimnames(md2) = list(paste0("b",1:10),paste0("a",1:12))
icmotif_count(md1,md2)

##input as weighted matrices,with row numbers as row names.
set.seed(12)
mdw1 <- matrix(sample(c(rep(0,40),runif(48,0,1))),8,11)
mdw2 <- matrix(sample(c(rep(0,40),runif(80,0,1))),10,12)
icmotif_count(mdw1,mdw2,weighted=TRUE)