Algebraic procedures for the analysis of multiple social networks are delivered with this package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>.
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"multiplex"makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. -
Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks.
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"multiplex"provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.
### create network data: two types of relations among three elements set.seed(123) arr <- round( replace( array(runif(18), c(3,3,2)), array(runif(18), c(3,3,2))>.5, 3 ) )### dichotomize data with customized cutoff value dichot(arr, c = 3)### string relations strings(arr) strings(arr, equat = TRUE, k = 3)### create numerical or symbolic semigroup semigroup(arr) semigroup(arr, type = "symbolic")### Green's relations of symbolic semigroup semigroup(arr, type = "symbolic") |> green.rel()### create the partial order strings(arr) |> partial.order(type = "strings")### plot partial order diagram require("Rgraphviz", quietly = TRUE) strings(arr) |> partial.order(type = "strings") |> diagram(type = "hasse")or equivalently:
### plot hasse diagram of the partial order require("Rgraphviz", quietly = TRUE) strings(arr) |> partial.order(type = "strings") |> hasse()(taken from the multiplex vignette)
### Fruits data frt <- data.frame(yellow = c(0,1,0,0,1,0,0,0), green = c(0,0,1,0,0,0,0,1), red = c(1,0,0,1,0,0,0,0), orange = c(0,0,0,0,0,1,1,0), apple = c(1,1,1,1,0,0,0,0), citrus = c(0,0,0,0,1,1,1,1)) rownames(frt) <- c("PinkLady", "GrannySmith", "GoldenDelicious", "RedDelicious", "Lemon", "Orange", "Mandarin", "Lime") ### Perform Galois connections among subsets with a reduced labeling galois(frt, labeling = "reduced")### Get the partial order of these "concepts" galois(frt, labeling = "reduced") |> partial.order(type = "galois")### Plot the concept lattice of the partial order require("Rgraphviz", quietly = TRUE) galois(frt, labeling = "reduced") |> partial.order(type = "galois") |> diagram(type = "concept")