Merge pull request #275 from snlab-ch/develop

Develop

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: migraph
 Title: Multimodal Network Analysis and More
-Version: 1.0.1
-Date: 2023-07-20
+Version: 1.1.0
+Date: 2023-10-03
 Description: A set of tools for analysing multimodal networks.
    It includes functions for measuring 
    centrality, centralization, cohesion, closure, constraint and diversity,
@@ -26,7 +26,7 @@ Imports:
     dplyr (>= 1.1.0),
     generics,
     ggplot2,
-    igraph,
+    igraph (>= 1.5.0),
     network,
     future,
     furrr,

NAMESPACE

@@ -118,11 +118,15 @@ export(network_dyad_census)
 export(network_eigenvector)
 export(network_equivalency)
 export(network_factions)
+export(network_harmonic)
 export(network_heterophily)
 export(network_homophily)
+export(network_indegree)
 export(network_length)
 export(network_mixed_census)
 export(network_modularity)
+export(network_outdegree)
+export(network_reach)
 export(network_reciprocity)
 export(network_reg)
 export(network_richness)
@@ -149,16 +153,20 @@ export(node_effsize)
 export(node_eigenvector)
 export(node_equivalence)
 export(node_fast_greedy)
+export(node_harmonic)
 export(node_heterophily)
 export(node_hierarchy)
 export(node_homophily)
+export(node_indegree)
 export(node_is_core)
 export(node_is_cutpoint)
 export(node_is_isolate)
 export(node_is_max)
 export(node_is_min)
 export(node_is_random)
 export(node_kernighanlin)
+export(node_outdegree)
+export(node_pagerank)
 export(node_path_census)
 export(node_power)
 export(node_quad_census)

NEWS.md

@@ -1,3 +1,22 @@
+# migraph 1.1.0
+
+## Package
+
+- Added Macports option to the README (closes #274, thank you @barracuda156)
+- Updates to the Centrality tutorial
+
+## Measures
+
+- Added `node_outdegree()`, `node_indegree()`, `network_outdegee()`, and `network_indegree()` wrappers
+- Added `network_reach()`
+- Added `node_harmonic()` and `network_harmonic()`
+- Added `node_pagerank()`
+- `plot.node_measure()` now returns a single plot for one-mode networks with a frequency histogram and a density overlay
+
+## Data
+
+- Upgraded all old igraph data to work with most recent version
+
 # migraph 1.0.1
 
 ## Measures

R/class_measures.R

@@ -105,19 +105,20 @@ plot.node_measure <- function(x, type = c("h", "d"), ...) {
                                   start = 0.7, end = 0.4)
     }
   } else {
-    if (type == "h") {
+    # if (type == "h") {
       p <- ggplot2::ggplot(data = data) +
         ggplot2::geom_histogram(ggplot2::aes(x = .data$Score),
                                 binwidth = ifelse(max(data$Score) > 1, 1,
                                                   ifelse(max(data$Score) > .1,
                                                          .1,
                                                          .01))) +
-        ggplot2::ylab("Frequency")
-    } else {
-      p <- ggplot2::ggplot(data = data) +
-        ggplot2::geom_density(ggplot2::aes(x = .data$Score)) +
-        ggplot2::ylab("Density")
-    }
+    #     ggplot2::ylab("Frequency")
+    # } else {
+    #   p <- ggplot2::ggplot(data = data) +
+        ggplot2::geom_density(ggplot2::aes(x = .data$Score), col = 2) +
+        ggplot2::scale_y_continuous("Frequency", 
+                                    sec.axis = ggplot2::sec_axis(~ ., breaks = c(0,1), name = "Density"))
+    # }
   }
   p + ggplot2::theme_classic() +
     ggplot2::theme(panel.grid.major = ggplot2::element_line(colour = "grey90"))

R/measure_centrality.R

@@ -115,6 +115,18 @@ node_degree <- function (.data, normalized = TRUE, alpha = 0,
   out
 }
 
+#' @describeIn degree_centrality Wraps node_degree(..., direction = "out")
+#' @export
+node_outdegree <- function (.data, normalized = TRUE, alpha = 0){
+  node_degree(.data, normalized = normalized, alpha = alpha, direction = "out")
+}
+
+#' @describeIn degree_centrality Wraps node_degree(..., direction = "in")
+#' @export
+node_indegree <- function (.data, normalized = TRUE, alpha = 0){
+  node_degree(.data, normalized = normalized, alpha = alpha, direction = "in")
+}
+
 #' @describeIn degree_centrality Calculate the degree centrality of edges in a network
 #' @examples 
 #' tie_degree(ison_adolescents)
@@ -166,6 +178,18 @@ network_degree <- function(.data, normalized = TRUE,
   out
 }
 
+#' @describeIn degree_centrality Wraps network_degree(..., direction = "out")
+#' @export
+network_outdegree <- function(.data, normalized = TRUE){
+  network_degree(.data, normalized = normalized, direction = "out")
+}
+
+#' @describeIn degree_centrality Wraps network_degree(..., direction = "out")
+#' @export
+network_indegree <- function(.data, normalized = TRUE){
+  network_degree(.data, normalized = normalized, direction = "in")
+}
+
 # Betweenness-like centralities ####
 
 #' Measures of betweenness-like centrality and centralisation
@@ -222,8 +246,8 @@ node_betweenness <- function(.data, normalized = TRUE,
 #' @examples
 #' (tb <- tie_betweenness(ison_adolescents))
 #' plot(tb)
-#' ison_adolescents %>% mutate_ties(weight = tb) %>% 
-#'   autographr()
+#' #ison_adolescents %>% mutate_ties(weight = tb) %>% 
+#' #   autographr()
 #' @export
 tie_betweenness <- function(.data, normalized = TRUE){
   if(missing(.data)) {expect_nodes(); .data <- .G()}
@@ -341,20 +365,39 @@ node_closeness <- function(.data, normalized = TRUE,
 #' @export
 node_reach <- function(.data, normalized = TRUE, k = 2){
   if(missing(.data)) {expect_nodes(); .data <- .G()}
-  out <- rowSums(node_path_census(.data)==k)
+  out <- rowSums(node_path_census(.data)<=k)
   if(normalized) out <- out/(manynet::network_nodes(.data)-1)
   out <- make_node_measure(out, .data)
   out
 }
 
+#' @describeIn close_centrality Calculate nodes' harmonic centrality or valued centrality.
+#'   This is thought to behave better than reach centrality for disconnected networks.
+#' @references
+#'   Marchiori, M, and V Latora. 2000. 
+#'   "Harmony in the small-world".
+#'   _Physica A_ 285: 539-546.
+#'   
+#'   Dekker, Anthony. 2005.
+#'   "Conceptual distance in social network analysis".
+#'   _Journal of Social Structure_ 6(3).
+#' @export
+node_harmonic <- function(.data, normalized = TRUE, k = -1){
+  if(missing(.data)) {expect_nodes(); .data <- .G()}
+  out <- igraph::harmonic_centrality(as_igraph(.data), # weighted if present
+                                     normalized = normalized, cutoff = k)
+  out <- make_node_measure(out, .data)
+  out
+}
+
 #' @describeIn close_centrality Calculate the closeness of each edge to each other edge
 #' in the network.
 #' @examples
 #' (ec <- tie_closeness(ison_adolescents))
 #' plot(ec)
-#' ison_adolescents %>% 
-#'   activate(edges) %>% mutate(weight = ec) %>% 
-#'   autographr()
+#' #ison_adolescents %>% 
+#' #   activate(edges) %>% mutate(weight = ec) %>% 
+#' #   autographr()
 #' @export
 tie_closeness <- function(.data, normalized = TRUE){
   if(missing(.data)) {expect_nodes(); .data <- .G()}
@@ -365,7 +408,7 @@ tie_closeness <- function(.data, normalized = TRUE){
   out
 }
 
-#' @describeIn close_centrality Calculate the closeness centralization for a graph
+#' @describeIn close_centrality Calculate a network's closeness centralization
 #' @examples
 #' network_closeness(ison_southern_women, direction = "in")
 #' @export
@@ -430,6 +473,26 @@ network_closeness <- function(.data, normalized = TRUE,
   out
 }
 
+#' @describeIn close_centrality Calculate a network's reach centralization
+#' @export
+network_reach <- function(.data, normalized = TRUE, k = 2){
+  if(missing(.data)) {expect_nodes(); .data <- .G()}
+  reaches <- node_reach(.data, normalized = FALSE, k = k)
+  out <- sum(max(reaches) - reaches)
+  if(normalized) out <- out / sum(manynet::network_nodes(.data) - reaches)
+  make_network_measure(out, .data)
+}
+
+#' @describeIn close_centrality Calculate a network's harmonic centralization
+#' @export
+network_harmonic <- function(.data, normalized = TRUE, k = 2){
+  if(missing(.data)) {expect_nodes(); .data <- .G()}
+  harm <- node_harmonic(.data, normalized = FALSE, k = k)
+  out <- sum(max(harm) - harm)
+  if(normalized) out <- out / sum(manynet::network_nodes(.data) - harm)
+  make_network_measure(out, .data)
+}
+
 # Eigenvector-like centralities ####
 
 #' Measures of eigenvector-like centrality and centralisation
@@ -450,6 +513,9 @@ NULL
 #'   Rather than performing this iteration,
 #'   most routines solve the eigenvector equation \eqn{Ax = \lambda x}.
 #' @param scale Logical scalar, whether to rescale the vector so the maximum score is 1. 
+#' @details
+#'   We use `{igraph}` routines behind the scenes here for consistency and because they are often faster.
+#'   For example, `igraph::eigencentrality()` is approximately 25% faster than `sna::evcent()`.
 #' @references 
 #'   Bonacich, Phillip. 1991. 
 #'   “Simultaneous Group and Individual Centralities.” 
@@ -467,6 +533,9 @@ node_eigenvector <- function(.data, normalized = TRUE, scale = FALSE){
                   manynet::tie_weights(.data), NA)
   graph <- manynet::as_igraph(.data)
 
+  if(!manynet::is_connected(.data)) 
+    warning("Unconnected networks will only allow nodes from one component have non-zero eigenvector scores.")
+
   # Do the calculations
   if (!manynet::is_twomode(graph)){
     out <- igraph::eigen_centrality(graph = graph, 
@@ -491,7 +560,7 @@ node_eigenvector <- function(.data, normalized = TRUE, scale = FALSE){
   out
 }
 
-#' @describeIn eigenv_centrality Calculate the power centrality of nodes in a network
+#' @describeIn eigenv_centrality Calculate the Bonacich, beta, or power centrality of nodes in a network
 #' @param exponent Decay rate for the Bonacich power centrality score.
 #' @section Power centrality:
 #'   Power or beta (or Bonacich) centrality 
@@ -574,14 +643,25 @@ node_alpha <- function(.data, alpha = 0.85){
                                              alpha = alpha),
                     .data)
 }
+
+#' @describeIn eigenv_centrality Calculate the pagerank centrality of nodes in a network
+#' @references 
+#'   Brin, Sergey and Page, Larry. 1998.
+#'   "The anatomy of a large-scale hypertextual web search engine".
+#'   _Proceedings of the 7th World-Wide Web Conference_. Brisbane, Australia.
+#' @export 
+node_pagerank <- function(.data){
+  if(missing(.data)) {expect_nodes(); .data <- .G()}
+  make_node_measure(igraph::page_rank(manynet::as_igraph(.data)),
+                    .data)
+}
 
 #' @describeIn eigenv_centrality Calculate the eigenvector centralization for a network
 #' @examples
 #' network_eigenvector(mpn_elite_mex)
 #' network_eigenvector(ison_southern_women)
 #' @export
 network_eigenvector <- function(.data, normalized = TRUE){
-  if(missing(.data)) {expect_nodes(); .data <- .G()}
   if (is_twomode(.data)) {
     out <- c(igraph::centr_eigen(manynet::as_igraph(manynet::to_mode1(.data)), 
                                  normalized = normalized)$centralization,
@@ -609,4 +689,3 @@ tie_eigenvector <- function(.data, normalized = TRUE){
 }
 
 
-

R/member_core.R

@@ -23,9 +23,9 @@
 #' “A Fast Algorithm for the Discrete Core/Periphery Bipartitioning Problem.”
 #' \doi{10.48550/arXiv.1102.5511}
 #' @examples 
-#' mpn_elite_usa_advice %>% as_tidygraph %>% 
-#'   mutate(corep = node_core(mpn_elite_usa_advice)) %>% 
-#'   autographr(node_color = "corep")
+#' #mpn_elite_usa_advice %>% as_tidygraph %>% 
+#' #   mutate(corep = node_core(mpn_elite_usa_advice)) %>% 
+#' #   autographr(node_color = "corep")
 #' network_core(mpn_elite_usa_advice)
 #' @export
 node_core <- function(.data){
@@ -47,4 +47,4 @@ node_core <- function(.data){
          1,2)
   if(manynet::is_labelled(.data)) names(out) <- manynet::node_names(.data)
   make_node_member(out, .data)
-}
+}

R/model_play.R

@@ -244,9 +244,11 @@ play_learning <- function(.data,
 #'   startValues[sample(seq_len(100), round(100*0.2))] <- NA
 #'   latticeEg <- manynet::create_lattice(100)
 #'   latticeEg <- manynet::add_node_attribute(latticeEg, "startValues", startValues)
-#'   manynet::autographr(latticeEg, node_color = "startValues", node_size = 5)
-#'   manynet::autographr(play_segregation(latticeEg, "startValues", 0.5), 
-#'              node_color = "startValues", node_size = 5)
+#'   latticeEg
+#'   play_segregation(latticeEg, "startValues", 0.5)
+#'   #manynet::autographr(latticeEg, node_color = "startValues", node_size = 5)
+#'   #manynet::autographr(play_segregation(latticeEg, "startValues", 0.5), 
+#'   #                    node_color = "startValues", node_size = 5)
 #' @export
 play_segregation <- function(.data, 
                              attribute,

README.Rmd

@@ -166,7 +166,7 @@ but also SIS, SIR, SIRS, SIER, and SIERS.
 ### Stable
 
 The easiest way to install the latest stable version of `{migraph}` is via CRAN.
-Simply open the R console and enter:
+Simply open the R console and enter:^[Macs with Macports installed may also install from the command line [using Macports](https://ports.macports.org/port/R-migraph/).]
 
 `install.packages('migraph')`
 
@@ -175,10 +175,6 @@ You can then begin to use `{migraph}` by loading the package:
 `library(migraph)`
 
 This will load any required packages and make the data contained within the package available.
-The version from CRAN also has all the vignettes built and included.
-You can check them out with:
-
-`vignettes(package = "migraph")`
 
 ### Development
 

README.md

@@ -173,7 +173,7 @@ SIS, SIR, SIRS, SIER, and SIERS.
 ### Stable
 
 The easiest way to install the latest stable version of `{migraph}` is
-via CRAN. Simply open the R console and enter:
+via CRAN. Simply open the R console and enter:[^1]
 
 `install.packages('migraph')`
 
@@ -182,10 +182,7 @@ You can then begin to use `{migraph}` by loading the package:
 `library(migraph)`
 
 This will load any required packages and make the data contained within
-the package available. The version from CRAN also has all the vignettes
-built and included. You can check them out with:
-
-`vignettes(package = "migraph")`
+the package available.
 
 ### Development
 
@@ -258,3 +255,6 @@ Most work on this package has been funded by the Swiss National Science
 Foundation (SNSF) [Grant Number
 188976](https://data.snf.ch/grants/grant/188976): “Power and Networks
 and the Rate of Change in Institutional Complexes” (PANARCHIC).
+
+[^1]: Macs with Macports installed may also install from the command
+    line [using Macports](https://ports.macports.org/port/R-migraph/).

cran-comments.md

@@ -10,5 +10,3 @@
 ## R CMD check results
 
 0 errors | 0 warnings | 0 notes
-
-- The Depends on `manynet` is required to offer full functionality to users