shiny animation unfinished

04-more.Rmd

@@ -47,16 +47,6 @@ Moreover, the DeGroot learning model uses **continuous** opinion values from 0 t
 > DeGroot, M. H. (1974). Reaching a Consensus. Journal of the American Statistical Association, 69(345), 118–121. https://doi.org/10.2307/2285509
 
 
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README.md

@@ -1,6 +1,6 @@
 # Linear Threshold Model (LTM) website
 
-Welcome to the Linear Threshold Model (LTM) Simulation repository! This project aims to provide a comprehensive exploration of the Linear Threshold Model and its applications in simulating influence propagation in social networks. The website link: 
+Welcome to the Linear Threshold Model (LTM) Simulation repository! This project aims to provide a comprehensive exploration of the Linear Threshold Model and its applications in simulating influence propagation in social networks. The website link: [https://wx-zhu.github.io/LinearThresholdModel/](https://wx-zhu.github.io/LinearThresholdModel/)
 
 ## Overview
 The Linear Threshold Model (LTM) is a widely used model in network science for studying information diffusion and influence maximization in social networks. This repository contains implementations of the LTM algorithm, as well as interactive shiny apps for simulating and visualizing influence propagation.

Shiny App/LTM_animation.R

@@ -126,6 +126,7 @@ activeNodes_list <- function(G, S, Ew, iterations) {
 
   for (i in 1:iterations) {
     T <- runLT(G, S, Ew)
+    # cat("--", i,"T:  ", T, "\n")
     total_active <- length(unique(T))  # Calculate the total active nodes in this iteration
     total_active_nodes[i] <- total_active  # Update total active nodes for current iteration
 
@@ -148,6 +149,7 @@ activeNodes_list <- function(G, S, Ew, iterations) {
   return(list(active_df = active_df, T_list = T_list))
 }
 
+
 # Function to calculate average size of activated nodes
 avgLT <- function(G, S, Ew, iterations = 1) {
   avgSize <- 0
@@ -159,7 +161,7 @@ avgLT <- function(G, S, Ew, iterations = 1) {
 }
 
 
-# Define the Greedy_LTM function
+# Greedy_LTM function to select k initial active nodes that maximize the local influence
 Greedy_LTM <- function(G, Ew, k, iterations) {
   start <- Sys.time()  # Record the start time
   S <- c()  # Initialize the seed set
@@ -235,7 +237,9 @@ ui <- fluidPage(
 
 # Server
 server <- function(input, output) {
-
+
+  active_df_selectedSeed <- reactiveVal(NULL)  # Define reactive variable
+
   observeEvent(input$run_simulation, {
     # Generate random graph based on user inputs
     if (input$graph_model == "erdos_renyi") {
@@ -247,23 +251,34 @@ server <- function(input, output) {
     # Calculate edge weights
     if (input$edge_weights == "uniform") {
       Ew <- uniformWeights(random_graph)
+      # Scale edge width based on the weights
+      edge_width <- sapply(E(random_graph), function(e) {
+        v <- ends(random_graph, e)[2]
+        Ew[[as.character(e)]]
+      })
     } else {
       Ew <- randomWeights(random_graph)
+      # Scale edge width based on the weights
+      edge_width <- sapply(E(random_graph), function(e) {
+        v <- ends(random_graph, e)[2]
+        Ew[[as.character(e)]]
+      })
     }
 
+    # Map edge_width to color_palette
+    color_palette <- wes_palette(n=5, name="Zissou1")
+    edge_color <- color_palette[cut(edge_width, breaks = 5)]
+
     # Run Greedy LTM function
     seed_set <- Greedy_LTM(random_graph, Ew, k = input$seed_nodes, iterations = input$iterations)
 
     # Generate animation plot
-    active_df_selectedSeed <- activeNodes_list(random_graph, seed_set, Ew, iterations = input$iterations)
-    T_list_with_seed <- c(list(seed_set), active_df_selectedSeed[["T_list"]])
-
-    # Initialize the plot
-    par(mar = c(6, 0, 0, 0) + .1)
+    active_df_selectedSeed(activeNodes_list(random_graph, seed_set, Ew, iterations = input$iterations))
 
     # Loop through each iteration to create the plot and save it as GIF frames
-    for (i in seq_along(T_list_with_seed)) {
-      T <- T_list_with_seed[[i]]
+    for (i in seq_along(active_df_selectedSeed()$T_list)) {
+      T <- active_df_selectedSeed()$T_list[[i]]
+      par(mar = c(6, 0, 0, 0) + .1)
       p <- plot.igraph(
         random_graph,
         edge.width = edge_width,
@@ -281,8 +296,8 @@ server <- function(input, output) {
 
     # Save the generated frames as a GIF
     saveGIF({
-      for (i in seq_along(T_list_with_seed)) {
-        T <- T_list_with_seed[[i]]
+      for (i in seq_along(active_df_selectedSeed()$T_list)) {
+        T <- active_df_selectedSeed()$T_list[[i]]
         p <- plot.igraph(
           random_graph,
           edge.width = edge_width,
@@ -299,16 +314,18 @@ server <- function(input, output) {
     }, movie.name = "LTM_animation_greedy.gif", clean = TRUE, fps = 4, fig.height = 400, fig.width = 600)
   })
 
+  # Move the GIF file into the www folder
+  file.copy("LTM_animation_greedy.gif", "www/LTM_animation_greedy.gif")
+
   # Output the generated GIF animation
   output$active_nodes_animation <- renderImage({
     list(src = "LTM_animation_greedy.gif",
          contentType = "image/gif")
   }, deleteFile = FALSE)
 
-
   # Output the table with the total number of active nodes at each iteration
   output$active_nodes_table <- renderDataTable({
-    active_df_selectedSeed$active_df
+    active_df_selectedSeed()$active_df
   }, rownames = FALSE, class = "hover")
 
 }

Shiny App/app.R

@@ -0,0 +1,11 @@
+library(shiny)
+
+ui <- fluidPage(
+  img(src="try1.gif")
+)
+
+server <- function(input, output, session) {
+
+}
+
+shinyApp(ui, server)