R implementation of narrative centrality measure for character interaction networks

narrative_centrality.R

narrative_centrality <- function(event_list, 
                                 chars = NULL,
                                 mode = "both",
                                 wp = 0.01,
                                 normalised = TRUE,
                                 start_at = 1) {
  C_in <- matrix(1, nrow(event_list), length(chars))
  C_out <- matrix(1, nrow(event_list), length(chars))
  C_in_norm <- matrix(1, nrow(event_list), length(chars))
  C_out_norm <- matrix(1, nrow(event_list), length(chars))
  if(is.null(chars)) {
    chars <- colnames(event_list)[start_at + 1:ncol(event_list)]
  }
  colnames(C_in) <- chars
  colnames(C_in_norm) <- chars
  colnames(C_out) <- chars
  colnames(C_out_norm) <- chars
  # Compute the scores
  C_in_t <- matrix(1, length(chars), 1)
  C_out_t <- matrix(1, length(chars), 1)
  for (t in 1:nrow(event_list)) {
    speaker <- event_list[t, start_at]
    receivers <- which(event_list[t, (start_at + 1):dim(event_list)[2]] == 1)
    C_in_t[receivers] <- C_in_t[receivers] + (wp * C_in_t[speaker])
    C_out_t[speaker] <- C_out_t[speaker] + 
      ((wp / length(receivers)) * sum(C_out_t[receivers]))
    for (c in 1:length(chars)) {
      C_in[t, c] <- C_in_t[c]
      C_out[t, c] <- C_out_t[c]
      C_in_norm[t, c] <- C_in_t[c] / sum(C_in_t)
      C_out_norm[t, c] <- C_out_t[c] / sum(C_out_t)
    }
  }
   # Return the requested values according to `normalised` and `mode`
  if(normalised == TRUE) {
    if(mode == "both") {
      return(list("out_scores" = C_out_norm, "in_scores" = C_in_norm))
    }
    if(mode == "in") {
      return(C_in_norm)
    }
    if(mode == "out") {
      return(C_out_norm)
    }
  } else {
    if(mode == "both") {
      return(list(C_out, C_in))
    }
    if(mode == "in") {
      return(C_in)
    }
    if(mode == "out") {
      return(C_out)
    }
  }
  
}