Differences

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--- krackhardt_datasets [2019/12/04 09:01] – [Using cutree] hkimscil
+++ krackhardt_datasets [2019/12/13 14:07] – hkimscil
@@ Line 65: / Line 65: @@
 <code>
-krack_friend <- delete.edges(krack_full, E(krack_full)[E(krack_full)$friendship_tie==0])
+krack_friend <- delete_edges(krack_full, E(krack_full)[E(krack_full)$friendship_tie==0])
 summary(krack_friend)
 krack_friend[]
-krack_advice <- delete.edges(krack_full, E(krack_full)[E(krack_full)$advice_tie==0])
+krack_advice <- delete_edges(krack_full, E(krack_full)[E(krack_full)$advice_tie==0])
 summary(krack_advice)
 krack_advice[]
-krack_reports_to <- delete.edges(krack_full, E(krack_full)[E(krack_full)$reports_to_tie==0])
+krack_reports_to <- delete_edges(krack_full, E(krack_full)[E(krack_full)$reports_to_tie==0])
 summary(krack_reports_to)
 krack_reports_to[]
@@ Line 213: / Line 213: @@
 <code>
-# Next, we'll use the same procedure to add social-interaction
+# Next, we'll use the same procedure to add advice
 # information.
 krack_advice_matrix_row_to_col <- get.adjacency(krack_advice, attr='advice_tie')
@@ Line 226: / Line 226: @@
 krack_advice_matrix <- rbind(krack_advice_matrix_row_to_col, krack_advice_matrix_col_to_row)
 krack_advice_matrix
+</code>
+<code>
+krack_friend_matrix_row_to_col <- get.adjacency(krack_friend, attr='friendship_tie')
+krack_friend_matrix_row_to_col
+# To operate on a binary graph, simply leave off the "attr"
+# parameter:
+krack_friend_matrix_row_to_col_bin <- get.adjacency(krack_friend)
+krack_friend_matrix_row_to_col_bin
+# For this lab, we'll use the valued graph. The next step is to
+# concatenate it with its transpose in order to capture both
+# incoming and outgoing task interactions.
+krack_friend_matrix_col_to_row <- t(as.matrix(krack_friend_matrix_row_to_col))
+krack_friend_matrix_col_to_row
+krack_friend_matrix <- rbind(krack_friend_matrix_row_to_col, krack_friend_matrix_col_to_row)
+krack_friend_matrix
+</code>
+<code>
+# ra (ar)
 krack_reports_to_advice_matrix <- rbind(krack_reports_to_matrix, krack_advice_matrix)
 krack_reports_to_advice_matrix
+# fa
+krack_friend_advice_matrix <- rbind(krack_friend_matrix, krack_advice_matrix)
+krack_friend_advice_matrix
+# far
+krack_friend_advice_reports_to_matrix <- rbind(krack_friend_advice_matrix, krack_reports_to_matrix)
+krack_friend_advice_reports_to_matrix
 </code>
 <code>
@@ Line 238: / Line 271: @@
 krack_reports_to_advice_cors <- cor(as.matrix(krack_reports_to_advice_matrix))
 krack_reports_to_advice_cors
+krack_friend_advice_cors <- cor(as.matrix(krack_friend_advice_matrix))
+krack_friend_advice_cors
+krack_friend_advice_reports_to_cors <- cor(as.matrix(krack_friend_advice_reports_to_matrix))
+krack_friend_advice_reports_to_cors
 </code>
@@ Line 246: / Line 287: @@
 # or equal to 0; thus, highly dissimilar (i.e., negatively
 # correlated) actors have higher values.
-dissimilarity <- 1 - krack_reports_to_advice_cors
+dissimilarity_ra <- 1 - krack_reports_to_advice_cors
-krack_reports_to_dist <- as.dist(dissimilarity)
+krack_reports_to_advice_dist <- as.dist(dissimilarity_ra)
-krack_reports_to_dist
+krack_reports_to_advice_dist
+dissimilarity_fa <- 1 - krack_friend_advice_cors
+krack_friend_advice_dist <- as.dist(dissimilarity_fa)
+krack_friend_advice_dist
+dissimilarity_rf <- 1 - krack_reports_to_friend_cors
+krack_reports_to_friend_dist <- as.dist(dissimilarity_rf)
+krack_reports_to_friend_dist
+dissimilarity_far <- 1 - krack_friend_advice_reports_to_cors
+krack_friend_advice_reports_to_dist <- as.dist(dissimilarity_far)
+krack_friend_advice_reports_to_dist
 # Note that it is also possible to use dist() directly on the
 # matrix. However, since cor() looks at associations between