Subplots
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@ -51,7 +51,8 @@ base_centralities[[2,1]] <- hipaa.deg %>% sort(decreasing = T)
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base_centralities[[3,1]] <- pci.deg %>% sort(decreasing = T)
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base_centralities[[3,1]] <- pci.deg %>% sort(decreasing = T)
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car_deg_Fn <- ( ecdf( car.deg ))
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car_deg_Fn <- ( ecdf( car.deg ))
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car_plots[[1,1]] <- car_deg_Fn(car.deg)*100
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#car_plots[[1,1]] <- car_deg_Fn(car.deg)*100
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car_plots[[1,1]] <- car.deg/max(car.deg)
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#### Katz
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#### Katz
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car.katz <- katz.cent(car)
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car.katz <- katz.cent(car)
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@ -195,7 +196,8 @@ tc_centralities[[2,1]] <- hipaa.tc.deg %>% sort(decreasing = T)
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tc_centralities[[3,1]] <- pci.tc.deg %>% sort(decreasing = T)
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tc_centralities[[3,1]] <- pci.tc.deg %>% sort(decreasing = T)
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car_tc_deg_Fn <- ( ecdf( car.tc.deg ))
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car_tc_deg_Fn <- ( ecdf( car.tc.deg ))
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car_plots[[2,1]] <- car_tc_deg_Fn(car.tc.deg)*100
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#car_plots[[2,1]] <- car_tc_deg_Fn(car.tc.deg)*100
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car_plots[[2,1]] <- car.tc.deg/max(car.tc.deg)
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#### Katz
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#### Katz
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car.tc.katz <- katz.cent(car.tc)
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car.tc.katz <- katz.cent(car.tc)
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@ -312,7 +314,8 @@ dtree_centralities[[2,1]] <- hipaa.dtree.deg %>% sort(decreasing = T)
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dtree_centralities[[3,1]] <- pci.dtree.deg %>% sort(decreasing = T)
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dtree_centralities[[3,1]] <- pci.dtree.deg %>% sort(decreasing = T)
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car_dt_deg_Fn <- ( ecdf( car.dtree.deg ))
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car_dt_deg_Fn <- ( ecdf( car.dtree.deg ))
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car_plots[[3,1]] <- car_dt_deg_Fn(car.dtree.deg)*100
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#car_plots[[3,1]] <- car_dt_deg_Fn(car.dtree.deg)*100
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car_plots[[3,1]] <-car.dtree.deg/max(car.dtree.deg)
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#### Katz
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#### Katz
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car.dtree.katz <- katz.cent(car.dtree)
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car.dtree.katz <- katz.cent(car.dtree)
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@ -603,12 +606,19 @@ car_dtree_df <- as.data.frame(car_plots[[3,1]])
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car_dtree_df$Node <- 1:nrow(as.data.frame(car_plots[[3,1]]))
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car_dtree_df$Node <- 1:nrow(as.data.frame(car_plots[[3,1]]))
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colnames(car_dtree_df) <- c("Centrality", "Node")
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colnames(car_dtree_df) <- c("Centrality", "Node")
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fig1 <- plot_ly(x=car_base_df$Node, y=car_base_df$Centrality, type="bar")
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fig1 <- plot_ly(x=car_base_df$Node, y=car_base_df$Centrality, type="bar", name="Base Network") %>%
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fig2 <- plot_ly(x=car_base_df$Node, y=car_tc_df$Centrality, type="bar")
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layout(xaxis = list(title = 'Node'))#, yaxis = list(title = 'Importance Percentile'))
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fig3 <- plot_ly(x=car_base_df$Node, y=car_dtree_df$Centrality, type="bar")
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# title=list(text="Base Network"))
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fig <- subplot(fig1, fig2, fig3, nrows = 3) %>%
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fig2 <- plot_ly(x=car_base_df$Node, y=car_tc_df$Centrality, type="bar", name="Transitive Closure Network") %>%
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layout(title = list(text = "Importance Distribution for Nodes
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layout(xaxis = list(title = 'Node'))#, yaxis = list(title = 'Importance Percentile'))
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in the Automobile Maintenance Network"),
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# title=list(text="Transitive Closure Network"))
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fig3 <- plot_ly(x=car_base_df$Node, y=car_dtree_df$Centrality, type="bar", name="Dominant Tree Network")%>%
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layout(xaxis = list(title = 'Node'))#, yaxis = list(title = 'Importance Percentile'))
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# title=list(text="Dominant Tree Network"))
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fig <- subplot(fig1, fig2, fig3, nrows = 3, shareX = TRUE, shareY = TRUE) %>%
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layout(title = list(text = "Degree Centrality Importance Distribution for
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Nodes in the Automobile Maintenance Network"),
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plot_bgcolor='#e5ecf6',
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plot_bgcolor='#e5ecf6',
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xaxis = list(
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xaxis = list(
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zerolinecolor = '#ffff',
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zerolinecolor = '#ffff',
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@ -617,7 +627,15 @@ fig <- subplot(fig1, fig2, fig3, nrows = 3) %>%
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yaxis = list(
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yaxis = list(
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zerolinecolor = '#ffff',
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zerolinecolor = '#ffff',
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zerolinewidth = 2,
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zerolinewidth = 2,
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gridcolor = 'ffff'))
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gridcolor = 'ffff'),
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annotations = list(
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list(x = -0.1 , y = 0.5, text = "Node Importance/max(Importance)",
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font = list(color = "black",size = 18),
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textangle = 270,
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showarrow = F, xref='paper', yref='paper', size=48)),
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showlegend=T
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)
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fig
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fig
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plot(car_plots[[1,1]], type="h", xlab="Node",
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plot(car_plots[[1,1]], type="h", xlab="Node",
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