Input data: USAir97
Start time: Tue Oct 07 08:46:06 2008
Calculates common social network measures on each selected input network.
Analysis for the Meta-Network
Individual entity classes have been combined into a single class, and all networks are combined to create a single network. If two networks connect the same entities, e.g. two agent x agent, then the links are combined. Link weights are made binary.
Row count 232 Column count 232 Link count 0 Density 0 Isolate count 232 Component count 232 Reciprocity 0 Characteristic path length 0 Clustering coefficient 0 Network levels (diameter) 0 Network fragmentation 1 Krackhardt connectedness 0 Krackhardt efficiency 1 Krackhardt hierarchy 1 Krackhardt upperboundedness 1 Degree centralization 0 Betweenness centralization 0 Closeness centralization 0
Min Max Average Stddev Total degree centrality 0 0 0 0 Total degree centrality (unscaled) 0 0 0 0 Eigenvector centrality 0 0 0 0 Hub centrality 0 0 0 0 Authority centrality 0 0 0 0 Betweenness centrality 0 0 0 0 Betweenness centrality (unscaled) 0 0 0 0 Clique membership count 0 0 0 0 Simmelian ties 0 0 0 0 Simmelian ties (unscaled) 0 0 0 0 Clustering coefficient 0 0 0 0 Key nodes
In-degree centrality
The In Degree Centrality of a node is its normalized in-degree.
Input network(s): meta-network
Rank Value Unscaled Nodes 1 0 0 All nodes have this value Out-degree centrality
The Out Degree Centrality of a node is its normalized out-degree.
Input network(s): meta-network
Rank Value Unscaled Nodes 1 0 0 All nodes have this value Total degree centrality
The Total Degree Centrality of a node is the normalized sum of its row and column degrees.
Input network(s): meta-network
Input network size: 232
Input network density: 0
Expected value from a random network of the same size and density: 0
Rank Value Unscaled Nodes Context* 1 0 0 All nodes have this value * Number of standard deviations from the mean if links were distributed randomly Mean: 0 Std.dev: 0 Eigenvector centrality
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central.
Input network(s): meta-network
Input network size: 232
Input network density: 0
Expected value from a random network of the same size and density: 0.209679
Rank Value Nodes Context* 1 0 All nodes have this value * Number of standard deviations from the mean if links were distributed randomly Mean: 0.209679 Std.dev: 0.178546 Betweenness centrality
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v.
Input network(s): meta-network
Input network size: 232
Input network density: 0
Expected value from a random network of the same size and density: 0.0220367
Rank Value Unscaled Nodes Context* 1 0 0 All nodes have this value * Number of standard deviations from the mean if links were distributed randomly Mean: 0.0220367 Std.dev: 0.155743 Closeness centrality
The average closeness of a node to the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network between the node and all other nodes.
Input network(s): meta-network
Input network size: 232
Input network density: 0
Expected value from a random network of the same size and density: -0.0105117
Rank Value Unscaled Nodes Context* 1 0.00431034 1.86595e-005 All nodes have this value * Number of standard deviations from the mean if links were distributed randomly Mean: -0.0105117 Std.dev: 0.0153064
Produced by ORA developed at CASOS - Carnegie Mellon University