Social Network Analysis With Igraph Package Using R - Revision history

RiccardoTasso at 11:43, 16 March 2011

2011-03-16T11:43:35Z

2010-06-20T15:00:50Z

2010-04-20T08:50:00Z

2010-04-20T08:44:27Z

‎Macroscopic Analysis

2010-04-20T08:44:20Z

‎Macroscopic Analysis

2010-04-20T08:44:08Z

‎Node Centrality Analysis

2010-04-20T08:39:22Z

‎Starting with the package

2010-04-20T08:36:20Z

2010-04-20T08:33:54Z

2010-04-03T11:33:27Z

‎Install the package under R environmemnt

@@ Line 14: / Line 14: @@
   <code>library('igraph')</code>
 parse a network file in Pajek format and access to its memory representation through the variable 'g':
-  <code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
+  <code>g <- read.graph(file='/your/path/.../file.net', [[pajek graph data format|format='pajek']])</code>
 list vertex and their attributes of graph g:
   <code>

@@ Line 36: / Line 36: @@
 == Macroscopic Analysis ==
-graph size (number of nodes): length(V(g))
+'''graph size''' (number of nodes): length(V(g))
-number of edges: length(E(g))
+'''number of edges''': length(E(g))
-diameter: diameter(g)
+'''diameter''': diameter(g)
-mean degree:
+'''mean degree''':
-density:
+'''density''':
-giant component size (the largest connected component size):
+'''giant component size''' (the largest connected component size):
 assortativity:
@@ Line 52: / Line 52: @@
 == Node Centrality Analysis ==
-degree: degree(g)
+'''degree''': degree(g)
-closeness: closeness(g)
+'''closeness''': closeness(g)
-betweenness: betweenness(g)
+'''betweenness''': betweenness(g)
-eigenvector/pagerank: evcent(g)
+'''eigenvector'''/'''pagerank''': evcent(g)
 == Clustering Techniques ==
-connected components: clusters(g)
+'''connected components''': clusters(g)
-edge betweenness:
+'''edge betweenness''':
-walktrap:
+'''walktrap''':
-spinglass:
+'''spinglass''':
 == References and further informations ==

@@ Line 52: / Line 52: @@
 == Node Centrality Analysis ==
-degree:
+degree: degree(g)
-closeness:
+closeness: closeness(g)
-betweenness:
+betweenness: betweenness(g)
-eigenvector/pagerank:
+eigenvector/pagerank: evcent(g)
 == Clustering Techniques ==
-connected components:
+connected components: clusters(g)
 edge betweenness:

← Older revision		Revision as of 08:44, 20 April 2010
Line 47:		Line 47:

	giant component size (the largest connected component size):		giant component size (the largest connected component size):
−

	assortativity:		assortativity:

← Older revision		Revision as of 08:44, 20 April 2010
Line 47:		Line 47:

	giant component size (the largest connected component size):		giant component size (the largest connected component size):
		+
		+
		+	assortativity:

	== Node Centrality Analysis ==		== Node Centrality Analysis ==

@@ Line 32: / Line 32: @@
 <code>
   plot(g)
 </code>

@@ Line 7: / Line 7: @@
   <code>R</code>
-type the command:
+type the command to download and install igraph as R library:
   <code>install.packages("igraph")</code>