Differences

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--- centrality [2015/11/30 14:00] – hkimscil
+++ centrality [2024/11/18 10:44] (current) – [Eigenvector] hkimscil
@@ Line 4: / Line 4: @@
 ===== Closeness =====
+<code>setwd("D:/Users/Hyo/Cs-Kant/CS/Classes/sna_examples/sna_in_r")</code>
 <code># Social Network Analysis: Closeness Centrality
 library(sna)
@@ Line 19: / Line 20: @@
 </code>
-<code>gplot(L, displaylabels = T, vertex.cex = 1, label.cex = 2, edge.col="red",
+<code>gplot(L, displaylabels = T, vertex.cex = 1, label.cex = 2, edge.col="blue",
-             boxed.labels = F, arrowhead.cex = 1, mode = "kamadakawai")
+boxed.labels = F, arrowhead.cex = 1, mode = "kamadakawai")
 </code>
 {{centrality_suzuki.png}}
@@ Line 64: / Line 65: @@
   - WEST: West group (private organization)
+for out link (information out)
 <code>round(closeness(knoke.infor,cmode="suminvdir"),2)
  [1] 0.70 0.89 0.83 0.70 0.94 0.63 0.65 0.81 0.65 0.78
 </code>
+for in link (information in)
 <code>round(closeness(t(knoke.infor),cmode="suminvdir"),2)
  [1] 0.76 0.94 0.72 0.78 0.94 0.46 1.00 0.59 0.78 0.61
@@ Line 93: / Line 96: @@
 </code>
-</code>betweenness(L)
+<code>betweenness(L)
 </code>
 <code>[1]  0  0  0 36 32 30 24 14  0
@@ Line 143: / Line 146: @@
 __Eigenvector Centrality__
   * 중요한 노드에 연결된 노드가 중요하다는 관점
+  * That is, the centrality of each node i is proportional to the sum of the centrality of its neighbors.
 <code>rownames(knoke.infor)
+</code>
+<code>> rownames(knoke.infor)
+ [1] "COUN" "COMM" "EDUC" "INDU" "MAYR" "WRO"  "NEWS" "UWAY" "WELF" "WEST"
 </code>
-<code>
+<code>round(evcent(knoke.infor),2)
+</code>
+<code>> round(evcent(knoke.infor),2)
+ [1] 0.25 0.41 0.36 0.26 0.47 0.15 0.22 0.35 0.21 0.33
 </code>
+<code>round(evcent(t(knoke.infor)),2)
+</code>
+<code>> round(evcent(t(knoke.infor)),2)
+ [1] 0.30 0.45 0.21 0.34 0.45 0.04 0.46 0.18 0.28 0.13
+</code>