Defines functions pagerank

Documented in pagerank

#' Estimate PageRank
#' @description Estimate PageRank (centrality scores) of nodes from an edge list or adjacency matrix. If data is a bipartite graph, estimates PageRank based on a one-mode projection of the input. If the data is an edge list, returns ranks ordered by the unique values in the supplied edge list (first by unique senders, then by unique receivers).
#' @details The default optional arguments are likely well-suited for most users. However, it is critical to change the is.bipartite function to FALSE when working with one mode data. In addition, when estimating PageRank in unipartite edge lists that contain nodes with outdegrees or indegrees equal to 0, it is recommended that users append self-ties to the edge list to ensure that the returned PageRank estimates are ordered intuitively.
#' @param data Data to use for estimating PageRank. Can contain unipartite or bipartite graph data, either formatted as an edge list (class data.frame, data.table, or tibble (tbl_df)) or as an adjacency matrix (class matrix or dgCMatrix).
#' @param is_bipartite Indicate whether input data is bipartite (rather than unipartite/one-mode). Defaults to TRUE.
#' @param project_mode Mode for which to return PageRank estimates. Parameter ignored if is_bipartite = FALSE. Defaults to "rows" (the first column of an edge list).
#' @param sender_name Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to first column of edge list.
#' @param receiver_name Name of sender column. Parameter ignored if data is an adjacency matrix. Defaults to the second column of edge list.
#' @param weight_name Name of edge weights. Parameter ignored if data is an adjacency matrix. Defaults to edge weights = 1.
#' @param rm_weights Removes edge weights from graph object before estimating PageRank. Defaults to FALSE.
#' @param duplicates How to treat duplicate edges if any in data. Parameter ignored if data is an adjacency matrix. If option "add" is selected, duplicated edges and corresponding edge weights are collapsed via addition. Otherwise, duplicated edges are removed and only the first instance of a duplicated edge is used. Defaults to "add".
#' @param return_data_frame Return results as a data frame with node names in the first column and ranks in the second column. If set to FALSE, the function just returns a named vector of ranks. Defaults to TRUE.
#' @param alpha Dampening factor. Defaults to 0.85.
#' @param max_iter Maximum number of iterations to run before model fails to converge. Defaults to 200.
#' @param tol Maximum tolerance of model convergence. Defaults to 1.0e-4.
#' @param verbose Show the progress of this function. Defaults to FALSE.
#' @keywords Bipartite PageRank rank centrality
#' @return A dataframe containing each node name and node rank. If return_data_frame changed to FALSE or input data is classed as an adjacency matrix, returns a vector of node ranks. Does not return node ranks for isolates.
#' @export
#' @import Matrix data.table
#' @md
#' @references
#' Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. "The pagerank citation ranking: Bringing order to the web". Technical report, Stanford InfoLab, 1999
#' @examples
#' #Prepare one-mode data
#'     df_one_mode <- data.frame(
#'       sender = sample(x = 1:10000, size = 10000, replace = TRUE),
#'       receiver = sample(x = 1:10000, size = 10000, replace = TRUE)
#'     )
#' #Add self-loops for all nodes
#'     unique_ids <- unique(c(df_one_mode$sender, df_one_mode$receiver))
#'     df_one_mode <- rbind(df_one_mode, data.frame(sender = unique_ids,
#'     receiver = unique_ids))
#' #Estimate PageRank in one-mode data
#'     PageRank <- pagerank(data = df_one_mode, is_bipartite = FALSE)
#' #Estimate PageRank in two-mode data
#'     df_two_mode <- data.frame(
#'       patient_id = sample(x = 1:10000, size = 10000, replace = TRUE),
#'       provider_id = sample(x = 1:5000, size = 10000, replace = TRUE)
#'     )
#'     PageRank <- pagerank(data = df_two_mode)

pagerank <- function(
  is_bipartite = TRUE,
  project_mode = c("rows", "columns"),
  sender_name = NULL,
  receiver_name = NULL,
  weight_name = NULL,
  rm_weights = FALSE,
  duplicates = c("add", "remove"),
  return_data_frame = TRUE,
  alpha = 0.85,
  max_iter = 200,
  tol = 1.0e-4,
  verbose = FALSE
  #a) if weight name = "unweighted", change to NULL
        if(weight_name == "unweighted"){
          weight_name <- NULL

  #b) convert to sparse matrix if a dataframe or matrix
      if(any(class(data) == "data.frame")){
        data <- data.table(data)
        if(verbose) message("Converting to sparse matrix...")
            adj_mat <- sparsematrix_from_edgelist(
              data = data,
              sender_name = sender_name,
              receiver_name = receiver_name,
              weight_name = weight_name,
              duplicates = duplicates[1],
              is_bipartite = T
          adj_mat <- sparsematrix_from_edgelist(
            data = data,
            sender_name = sender_name,
            receiver_name = receiver_name,
            weight_name = weight_name,
            duplicates = duplicates[1],
            is_bipartite = F

      }else if(length(class(data)) == 1 & class(data) == "matrix"){
        adj_mat <- sparsematrix_from_matrix(data)
        data <- data[rowSums(data) != 0, rowSums(data) != 0]
      }else if(class(data) == "dgCMatrix" | class(data) == "dsCMatrix"){
        adj_mat <- data
      else if(class(data) != "dgCMatrix" & class(data) != "dsCMatrix"){
        stop('data is not a data.frame, tbl_df, data.table, matrix, dgCMatrix, or dsCMatrix')

  #c) project to one mode
          if(verbose) message("Projecting to one mode...")
          adj_mat <- project_to_one_mode(adj_mat = adj_mat, mode = project_mode[1])
          if(verbose) message("Treating input as one mode...")


  #d) remove weights
      if(verbose) message("Removing edge weights...")
      adj_mat <- sparsematrix_rm_weights(adj_mat)

  #d) estimate pagerank
      if(verbose) message("Estimating pagerank...")
      rank <- pagerank_from_matrix(
        adj_mat = adj_mat,
        alpha = alpha,
        max_iter = max_iter,
        tol = tol,
        verbose = verbose

  #e) find rank labels
      #i) get labels if data is data frame
          if(any(class(data) == "data.frame")){
            #1) determine ID index
                if(is.null(sender_name) | is.null(receiver_name)){
                    id1 = 1
                    id2 = 2
                    id1 = match(sender_name, names(data))
                    id2 = match(receiver_name, names(data))
            #2) pull IDs for each mode in order of function
                edges <- data[, c(id1, id2), with = F]
                id_names1 <- as.character(unlist(unique(data[, id1, with = F])))
                id_names2 <- as.character(unlist(unique(data[, id2, with = F])))
                  id_names1 <- unique(c(id_names1, id_names2))
                  id_names2 <- id_names1
      #ii) get labels if data is matrix
          if(!any(class(data) == "data.frame")){

  #f) format results
      #i) get variable name id of a data.frame
          if(any(class(data) == "data.frame")){
            sender_name <- names(edges)[1]
            receiver_name <- names(edges)[2]
          } else{
            sender_name <- "ID"
            receiver_name <- "ID"

      #ii) if return a data frame, format results as a dataframe
            if(project_mode[1] == "rows"){
              rank <- data.frame(ID = id_names1, rank = rank)
              names(rank)[1] <- sender_name
            if(project_mode[1] == "columns"){
              rank <- data.frame(ID = id_names2, rank = rank)
              names(rank)[1] <- receiver_name
      #iii) if not return data frame, format results as vector
            if(project_mode[1] == "rows"){
              names(rank) <- id_names1
            if(project_mode[1] == "columns"){
              names(rank) <- id_names2

  #g) return data


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birankr documentation built on March 24, 2020, 1:08 a.m.