R/ch_coauthor.R

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

#' Swiss political science co-authorship network 2013
#'
#' Swiss political science co-authorship network 2013
#'
#' The Swiss political science co-authorship network 2013 dataset contains the
#' co-authorship network of all political scientists at Swiss universities and
#' research institutes in late 2013. The data are described in Leifeld and
#' Ingold (2016) and Leifeld (2018). The data contained here include
#' postdoctoral and professorial researchers but not PhD students, as in Leifeld
#' (2018), without the PhD researchers included in Leifeld and Ingold (2016).
#' For the full dataset, see the replication archive at DOI
#' \doi{10.7910/DVN/85SK1M}.
#'
#' Leifeld and Ingold (2016) summarize the data collection strategy as follows:
#' \emph{"Data gathering took place between July and December 2013. A single
#' coder pursued a three-step coding procedure: he first created a list of all
#' relevant university departments and research institutes that host political
#' scientists in Switzerland, then he browsed the websites of these institutes
#' and entered all researchers along with several details about them into a
#' database, including their seniority status (predoctoral, postdoctoral, or
#' professor) and the URL of their publication list (either the CV, the
#' institutional website, a private homepage, or several of those items in order
#' to get a complete publication profile of each person). After entering all
#' researchers of an institute, the coder went through the researchers'
#' publication lists and entered the following pieces of information for each
#' publication into the database: the reporting author, the names of all
#' co-authors, the title of the publication, the year, the name of the journal
#' or book in which the publication appeared (if applicable), the names of all
#' editors (if applicable), and a classification of the type of publication
#' (academic journal, book chapter, monograph, edited volume, other). Most
#' publications are relatively recent, but the earliest publications in the
#' database date back to the 1960s. After completing these three steps, data
#' entered at the beginning was double-checked in order to avoid bias due to new
#' publications that may have shown up during the coding time period. This
#' procedure is the best one can do in terms of completeness, but it should be
#' clear that it crucially depends on the accuracy of the self-reported
#' bibliographic information. For example, if a researcher did not update his or
#' her CV or list of publications for the previous six months, those most recent
#' publications only had a chance to enter the database if the co-authors listed
#' the publication on their website. In some relatively rare cases, all authors
#' failed to report recent updates, and this may cause minor inaccuracies in the
#' network dataset, mostly affecting very recent publications in 2013 because
#' there is, on average, a reporting lag."}
#'
#' Based on the collected publication data, a co-authorship network matrix with
#' 156 nodes was created. In addition to this matrix, the dataset here contains
#' node attribute data (institutional affiliations, location, demographics,
#' language shares, publication type shares) and relational covariates
#' (geographical distance, similarity in terms of the share of English articles,
#' and topic similarity) as described in Leifeld (2018). The dataset can be used
#' to replicate Leifeld (2018), but only approximately due to changes in the
#' estimation routine in the \pkg{ergm} package since the article was published.
#'
#' @name coauthor
#'
#' @aliases ch_coauthor ch_coaut ch_dist100km ch_en_article_sim ch_nodeattr
#'   ch_topicsim
#'
#' @docType data
#'
#' @format
#' \describe{
#' \item{\code{ch_coaut}}{is an undirected, weighted 156 x 156 adjancency matrix
#' indicating how many publications political scientist in Switzerland shared
#' with each other as reported in late 2013, including only postdoctoral and
#' professorial political scientists affiliated with research institutes or
#' universities. The exact edge weight should be treated with caution because
#' some publications were counted multiple times because they were reported by
#' multiple co-authors. The diagonal contains the number of publications of
#' the respective author. Leifeld and Ingold (2016) describe the data collection
#' process in more detail.}
#'
#' \item{\code{ch_nodeattr}}{is a data frame with node attributes/variables for
#' the 156 researchers, in the same alphabetical row order as the network
#' matrix. The first twelve columns with column labels starting with "inst_" are
#' affiliations with different institutions (1 = affiliated; 0 = no
#' affiliation). The next seven columns with column labels starting with "city_"
#' contain the locations of the researchers' institutional affiliations. The
#' "phdyear" column contains the self-reported year of obtaining the PhD, and
#' the "birthyear" column contains the self-reported or publicly available year
#' of birth; these two variables contain many missing values. The "status"
#' column indicates whether a researcher was listed as a professor or as having
#' postdoctoral or other non-professorial status at the time. "chairtitle" is
#' the name of the chair or research group the researcher reported to be a
#' member of. "num_publications" is the total number of publications,
#' "num_articles" the number of journal articles among them, "num_books" the
#' number of books among them, "share_articles" the percentage of journal
#' articles among the publications, and "share_books" the percentage of
#' monographs and edited volumes among the publications. The four columns with
#' column names starting with "lang_" contain the relative shares of English,
#' French, German, Italian, and other languages among the publications of the
#' researcher. The column "share_en_articles" contains the percentage of English
#' journal articles among all publications of the researcher. "male" is a dummy
#' variable indicating whether the author is male (1) or female (0). The
#' variables contained here are described in Leifeld (2018).}
#'
#' \item{\code{ch_dist100km}}{is a 156 x 156 matrix containing the geographical
#' distance between any two researchers measured in units of 100km (for a
#' reasonable scaling of coefficients in a statistical model), computed over the
#' latitude and longitude of their main institutional affiliations. The measure
#' is included in Leifeld (2018).}
#'
#' \item{\code{ch_en_article_sim}}{is a 156 x 156 matrix containing the
#' similarity between any two researchers in terms of the share of their work
#' that is published in English and as journal articles. Values closer to 1.0
#' indicate that two researchers were similar in their language and publication
#' type portfolio while values closer to 0 indicate that they were relatively
#' dissimilar. Only extra-dyadic publications were counted in establishing this
#' similarity. I.e., if researcher A and B co-authored, their joint publications
#' were not included in establishing their English article share similarity.
#' This was done to reduce endogeneity/reverse causality when modeling
#' co-authorship as a function of English article share similarity. The measure
#' is described in Leifeld (2018).}
#'
#' \item{\code{ch_topicsim}}{is a 156 x 156 topic similarity matrix for the
#' researchers. Topic similarities were computed by taking into account all
#' words in the publication titles of any two researchers, excluding the
#' publications they published as co-authors (i.e., only extra-dyadic
#' publications, to reduce endogeneity/reverse causality in modeling
#' co-authorship ties as a function of topic similarity). Topic similarity was
#' established by computing the cosine similarity between the tf-idf scores for
#' the title words of any two researchers (i.e., a vector space model). Leifeld
#' (2018) contains more details on this procedure.}
#' }
#'
#' @references
#' Leifeld, Philip (2018): Polarization in the Social Sciences: Assortative
#' Mixing in Social Science Collaboration Networks is Resilient to
#' Interventions. \emph{Physica A: Statistical Mechanics and its Applications}
#' 507: 510--523. \doi{10.1016/j.physa.2018.05.109}. Full replication data:
#' \doi{10.7910/DVN/85SK1M}.
#'
#' Leifeld, Philip and Karin Ingold (2016): Co-authorship Networks in Swiss
#' Political Research. \emph{Swiss Political Science Review} 22(2): 264--287.
#' \doi{10.1111/spsr.12193}.
#'
#' @source The data were collected from public information online. The full
#' data collection details are described in Leifeld and Ingold (2016).
#'
#' @keywords datasets
#'
#' @examples
#' \dontrun{
#' # Replication code for the full Swiss co-authorship ERGM in Leifeld (2018).
#' # Note that the estimates can only be reproduced approximately due to
#' # internal changes in the ergm package.
#'
#' library("network")
#' library("ergm")
#'
#' data("ch_coauthor")
#'
#' # set up network object with node attributes
#' ch_nw <- network(ch_coaut, directed = FALSE)
#' set.vertex.attribute(ch_nw, "frequency", ch_nodeattr$num_publications)
#' set.vertex.attribute(ch_nw, "status", as.character(ch_nodeattr$status))
#' set.vertex.attribute(ch_nw, "male", ch_nodeattr$male)
#' set.vertex.attribute(ch_nw, "share_en_articles",
#'                      ch_nodeattr$share_en_articles)
#'
#' # create same affiliation matrix
#' ch_inst_indices <- which(grepl("^inst_.+", colnames(ch_nodeattr)))
#' ch_same_affiliation <- as.matrix(ch_nodeattr[, ch_inst_indices]) %*%
#'   t(ch_nodeattr[, ch_inst_indices])
#'
#' # create same chair matrix
#' ch_nodeattr$chairtitle[ch_nodeattr$chairtitle == ""] <- NA
#' ch_same_chair <- matrix(0, nrow = nrow(ch_same_affiliation),
#'                         ncol = ncol(ch_same_affiliation))
#' for (i in 1:length(ch_nodeattr$chairtitle)) {
#'   for (j in 1:length(ch_nodeattr$chairtitle)) {
#'     if (i != j &&
#'         !is.na(ch_nodeattr$chairtitle[i]) &&
#'         !is.na(ch_nodeattr$chairtitle[j]) &&
#'         ch_nodeattr$chairtitle[i] == ch_nodeattr$chairtitle[j] &&
#'         ch_same_affiliation[i, j] == TRUE) {
#'       ch_same_chair[i, j] <- 1
#'     }
#'   }
#' }
#' rownames(ch_same_chair) <- rownames(ch_same_affiliation)
#' colnames(ch_same_chair) <- colnames(ch_same_affiliation)
#'
#' # create supervision matrix (same chair + affiliation + mixed seniority)
#' ch_supervision <- ch_same_affiliation *
#'   ch_same_chair *
#'   matrix(ch_nodeattr$status == "professor",
#'          nrow = nrow(ch_same_chair),
#'          ncol = ncol(ch_same_chair),
#'          byrow = FALSE) *
#'   matrix(ch_nodeattr$status != "professor",
#'          nrow = nrow(ch_same_chair),
#'          ncol = ncol(ch_same_chair),
#'          byrow = TRUE)
#'
#' # ERGM estimation
#' ch_model <- ergm(ch_nw ~
#'                    edges +
#'                    gwesp(0.3, fixed = TRUE) +
#'                    gwdegree(0.4, fixed = TRUE) +
#'                    nodecov("frequency") +
#'                    nodefactor("status") +
#'                    nodefactor("male") +
#'                    nodematch("male") +
#'                    edgecov(ch_dist100km) +
#'                    edgecov(ch_same_affiliation) +
#'                    edgecov(ch_same_chair) +
#'                    edgecov(ch_supervision) +
#'                    edgecov(ch_topicsim) +
#'                    nodecov("share_en_articles") +
#'                    edgecov(ch_en_article_sim),
#'                  control = control.ergm(MCMLE.termination = "Hummel",
#'                                         MCMLE.effectiveSize = NULL))
#' summary(ch_model)  # corresponds Column 1 in Table 3 in Leifeld (2018)
#' }
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