STANDARD NETWORK ANALYSIS REPORT

STANDARD NETWORK ANALYSIS REPORT

Input data: krebs_anytie

Start time: Mon Oct 06 10:33:22 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 count73
Column count73
Link count0
Density0
Isolate count73
Component count73
Reciprocity0
Characteristic path length0
Clustering coefficient0
Network levels (diameter)0
Network fragmentation1
Krackhardt connectedness0
Krackhardt efficiency1
Krackhardt hierarchy1
Krackhardt upperboundedness1
Degree centralization0
Betweenness centralization0
Closeness centralization0
MinMaxAverageStddev
Total degree centrality0000
Total degree centrality (unscaled)0000
Eigenvector centrality0000
Hub centrality0000
Authority centrality0000
Betweenness centrality0000
Betweenness centrality (unscaled)0000
Clique membership count0000
Simmelian ties0000
Simmelian ties (unscaled)0000
Clustering coefficient0000

Key nodes

In-degree centrality

The In Degree Centrality of a node is its normalized in-degree.

Input network(s): meta-network

RankValueUnscaledNodes
100All nodes have this value

Out-degree centrality

The Out Degree Centrality of a node is its normalized out-degree.

Input network(s): meta-network

RankValueUnscaledNodes
100All 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: 73

Input network density: 0

Expected value from a random network of the same size and density: 0

RankValueUnscaledNodesContext*
100All 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: 73

Input network density: 0

Expected value from a random network of the same size and density: 0.177236

RankValueNodesContext*
10All nodes have this value
* Number of standard deviations from the mean if links were distributed randomly
Mean: 0.177236
Std.dev: 0.181396

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: 73

Input network density: 0

Expected value from a random network of the same size and density: 0.0747959

RankValueUnscaledNodesContext*
100All nodes have this value
* Number of standard deviations from the mean if links were distributed randomly
Mean: 0.0747959
Std.dev: 0.157002

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: 73

Input network density: 0

Expected value from a random network of the same size and density: -0.0126301

RankValueUnscaledNodesContext*
10.01369860.000190259All nodes have this value
* Number of standard deviations from the mean if links were distributed randomly
Mean: -0.0126301
Std.dev: 0.0296534

Produced by ORA developed at CASOS - Carnegie Mellon University