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util-networks-misc.R
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## This file is part of coronet, which is free software: you
## can redistribute it and/or modify it under the terms of the GNU General
## Public License as published by the Free Software Foundation, version 2.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License along
## with this program; if not, write to the Free Software Foundation, Inc.,
## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
##
## Copyright 2016-2017 by Sofie Kemper <kemperso@fim.uni-passau.de>
## Copyright 2016-2017 by Claus Hunsen <hunsen@fim.uni-passau.de>
## Copyright 2016-2018 by Thomas Bock <bockthom@fim.uni-passau.de>
## Copyright 2020, 2023-2024 by Thomas Bock <bockthom@cs.uni-saarland.de>
## Copyright 2017 by Angelika Schmid <schmidang@fim.uni-passau.de>
## Copyright 2019 by Jakob Kronawitter <kronawij@fim.uni-passau.de>
## Copyright 2019-2020 by Anselm Fehnker <anselm@muenster.de>
## Copyright 2024 by Leo Sendelbach <s8lesend@stud.uni-saarland.de>
## Copyright 2024 by Maximilian Löffler <s8maloef@stud.uni-saarland.de>
## All Rights Reserved.
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Libraries ---------------------------------------------------------------
requireNamespace("parallel") # for parallel computation
requireNamespace("igraph") # networks
requireNamespace("Matrix") # for sparse matrices
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Get active authors -----------------------------------------------------
#' Get all author names that are active in at least one of the networks.
#'
#' @param networks the list of networks from which the author names are wanted
#' @param globally decides if all author names are in one list or in separate lists for each network [default: TRUE]
#'
#' @return the list of author names as a list of sorted vectors
get.author.names.from.networks = function(networks, globally = TRUE) {
## for each network, get a list of authors that are in this network
active.authors.list = lapply(networks, function(network) {
active.authors = igraph::V(network)$name
active.authors = sort(active.authors)
return(active.authors)
})
if (globally) {
## flatten the list of lists to one list of authors
active.authors = unlist(active.authors.list, recursive = FALSE)
## remove distracting named list members
names(active.authors) = NULL
## remove duplicates and order alphabetically ascending
active.authors = active.authors[!duplicated(active.authors)]
active.authors = sort(active.authors)
## return as a list
return(list(active.authors))
} else {
return(active.authors.list)
}
}
#' Get all author names that are active in at least one of the data sources during the data ranges.
#'
#' @param data.ranges the list of the data ranges
#' @param data.sources the data sources from which the author names should be retrieved,
#' can be either \code{"commits"}, \code{"mails"}, or \code{"issues"},
#' or any combination of them [default: c("commits", "mails", "issues")]
#' @param globally decides if all author names are in one list or in separate for each network [default: TRUE]
#'
#' @return the list of author names as a list of sorted vectors
get.author.names.from.data = function(data.ranges, data.sources = c("commits", "mails", "issues"), globally = TRUE) {
data.sources = match.arg.or.default(data.sources, several.ok = TRUE)
## for each range, get the authors who have been active on at least one data source in this range
active.authors.list = lapply(data.ranges, function(range.data) {
active.authors = range.data$get.authors.by.data.source(data.sources)
active.authors.names = sort(active.authors[["author.name"]])
return(active.authors.names)
})
if (globally) {
## flatten the list of lists to one list of authors
active.authors = unlist(active.authors.list, recursive = FALSE)
## remove distracting named list members
names(active.authors) = NULL
## remove duplicates and order alphabetically ascending
active.authors = active.authors[!duplicated(active.authors)]
active.authors = sort(active.authors)
## return as a list
return(list(active.authors))
} else {
return(active.authors.list)
}
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Adjacency matrices ----------------------------------------------------
#' Get a sparse expanded adjacency matrix (in triplet format) for a given network.
#'
#' The adjacency matrix is expanded as it may contain rows and columns for authors which are not part of the network
#' but given in the \code{authors} parameter. However, this also means that authors present in the network
#' but not given in the \code{authors} parameter are not contained in the expanded adjacency matrix.
#'
#' @param network the given network
#' @param authors all authors that are wanted in the adjacency matrix
#' @param weighted decides if the adjacency matrix shall be weighted [default: FALSE]
#'
#' @return the sparse adjacency matrix of the network
get.expanded.adjacency = function(network, authors, weighted = FALSE) {
## create an empty sparse matrix using the triplet form with the right size.
## x = 0 indicates that the matrix should contain numeric values (i.e., it is a 'dgTMatrix';
## without setting x = 0 it would be a binary 'ngTMatrix')
matrix = Matrix::sparseMatrix(i = c(), j = c(), x = 0, dims = c(length(authors), length(authors)), repr = "T")
## add row and column names
rownames(matrix) = authors
colnames(matrix) = authors
if (igraph::vcount(network) > 0) {
if (weighted) {
## get the weighted adjacency matrix for the current network
matrix.data = igraph::as_adjacency_matrix(network, attr = "weight")
} else {
## get the unweighted sparse adjacency matrix for the current network
matrix.data = igraph::as_adjacency_matrix(network)
}
network.authors.num = nrow(matrix.data)
## order the adjacency matrix and filter out authors that were not in authors list
if (nrow(matrix.data) > 1) { # for a 1x1 matrix ordering does not work
matrix.data = matrix.data[order((rownames(matrix.data)[rownames(matrix.data) %in% authors])),
order((rownames(matrix.data)[rownames(matrix.data) %in% authors]))]
}
if (network.authors.num > nrow(matrix.data)) {
# write a warning with the number of authors from the network that we ignore
warning.string = sprintf("The network had %d authors that will not be displayed in the matrix!",
network.authors.num - nrow(matrix.data))
logging::logwarn(warning.string)
}
## save the activity data per author
if (nrow(matrix.data) > 0) {
matrix[rownames(matrix.data), colnames(matrix.data)] = matrix.data
}
if (!weighted) {
matrix[matrix > 0] = 1
}
}
return(matrix)
}
#' Calculates a sparse adjacency matrix for each network in the list.
#' All adjacency matrices are expanded in such a way that they use the same set
#' of authors derived from all networks in the list.
#'
#' @param networks list of networks
#' @param weighted decides if the adjacency matrix shall be weighted [default: FALSE]
#'
#' @return the list of adjacency matrices
get.expanded.adjacency.matrices = function(networks, weighted = FALSE){
authors = get.author.names.from.networks(networks)[[1]]
adjacency.matrices = parallel::mclapply(networks, get.expanded.adjacency, authors, weighted)
return(adjacency.matrices)
}
#' Gets a list of networks, converts them to sparse adjacency matrices, and sums up the adjacency matrices cumulatively.
#' This means that the first entry of the returned list is just the adjacency matrix from the first network,
#' the second entry is the sum of the first and the second entry, and so on.
#'
#' @param networks list of networks
#' @param weighted decides if the adjacency matrix shall be weighted [default: FALSE]
#'
#' @return the list of cumulated adjacency matrices
get.expanded.adjacency.cumulated = function(networks, weighted = FALSE) {
## get expanded adjacency matrices first
matrices = get.expanded.adjacency.matrices(networks, weighted)
## pair-wise sum of matrices: m.cumul(n) = m.cumul(m - 1) + m
## (intermediate results consecutively stored in matrices.cumulated)
matrices.cumulated = list(matrices[[1]]) # first one is complete already
if (length(matrices) > 1) {
for (m in 2:(length(matrices))){
matrices.cumulated[[m]] = matrices.cumulated[[m - 1]] + matrices[[m]]
rownames(matrices.cumulated[[m]]) = rownames(matrices.cumulated[[m - 1]])
colnames(matrices.cumulated[[m]]) = colnames(matrices.cumulated[[m - 1]])
if (!weighted) {
## search for a non-zero entry and set them to an arbitray number (e.g., 42)
## to force that all non-zero entries are correctly set to 1 afterwards
if (length(matrices.cumulated[[m]]@i) > 0) {
## the first non-zero entry of a sparse matrix is at the first position pointed to by
## the lists @i and @j of the matrix. Since these lists store the position 0-based,
## but the list access we use them for is 1-based, we need to add 1 to both values.
row = matrices.cumulated[[m]]@i[[1]] + 1
col = matrices.cumulated[[m]]@j[[1]] + 1
matrices.cumulated[[m]][row][col] = 42
matrices.cumulated[[m]]@x = rep(1, length(matrices.cumulated[[m]]@i))
}
}
}
}
return(matrices.cumulated)
}
#' Converts a list of adjacency matrices to an array.
#' Expects matrices of equal dimension with equal column- and rownames.
#'
#' @param adjacency.list the list of adjacency matrices
#'
#' @return the converted array
convert.adjacency.matrix.list.to.array = function(adjacency.list){
if (length(adjacency.list) < 1) {
logging::logerror("The method 'convert.adjacency.matrix.list.to.array received' an empty list!")
stop("The method 'convert.adjacency.matrix.list.to.array' received an empty list!")
}
## Check if all matrices have equal column- and rownames
rownames = rownames(adjacency.list[[1]])
colnames = colnames(adjacency.list[[1]])
if (length(adjacency.list) > 1) {
for (i in 2:length(adjacency.list)) {
if (!identical(rownames, rownames(adjacency.list[[i]])) || !identical(colnames, colnames(adjacency.list[[i]]))) {
error.string = sprintf("The matrix at position %d has different col or rownames from the first!", i)
logging::logerror(error.string)
stop(error.string)
}
}
}
## create a 3-dimensional array representing the adjacency matrices (SIENA data format) as result
array = array(data = 0, dim = c(nrow(adjacency.list[[1]]), nrow(adjacency.list[[1]]), length(adjacency.list)))
rownames(array) = rownames
colnames(array) = colnames
## copy the activity values from the adjacency matrices in the list to the corresponding array slices
for (i in seq_along(adjacency.list)) {
adjacency = adjacency.list[[i]]
activity.indices = Matrix::which(adjacency != 0, arr.ind = TRUE)
for (j in seq_len(nrow(activity.indices))) {
array[as.vector(activity.indices[j, 1]), as.vector(activity.indices[j, 2]), i] =
adjacency[as.vector(activity.indices[j, 1]), as.vector(activity.indices[j, 2])]
}
}
return(array)
}