STANDARD NETWORK ANALYSIS REPORT

STANDARD NETWORK ANALYSIS REPORT

Input data: Faux Mesa High

Start time: Thu Sep 13 15:51:44 2012

Transformations applied to network Faux Mesa High:

Thu Sep 13 10:47:55 2012 - symmetrized network Faux Mesa High

Calculates common social network measures on each selected input network.

Network Agent x Agent

Network Level Measures

MeasureValue
Row count205.000
Column count205.000
Link count202.000
Density0.010
Isolate count58.000
Component count69.000
Reciprocity1.000
Characteristic path length6.811
Clustering coefficient0.086
Network levels (diameter)16.000
Network fragmentation0.657
Krackhardt connectedness0.343
Krackhardt efficiency0.991
Krackhardt hierarchy0.000
Krackhardt upperboundedness1.000
Degree centralization0.054
Betweenness centralization0.168
Closeness centralization0.006
Reciprocal?Yes

Node Level Measures

MeasureMinMaxAvgStddev
Total degree centrality0.0000.0640.0100.010
Total degree centrality [Unscaled]0.00026.0003.9414.246
In-degree centrality0.0000.0630.0100.010
In-degree centrality [Unscaled]0.00013.0001.9712.123
Out-degree centrality0.0000.0630.0100.010
Out-degree centrality [Unscaled]0.00013.0001.9712.123
Eigenvector centrality0.0001.0000.1600.312
Closeness centrality0.0050.0110.0090.003
Closeness centrality [Unscaled]0.0000.0000.0000.000
Betweenness centrality0.0000.1770.0100.025
Betweenness centrality [Unscaled]0.0007323.914406.2831023.370
Hub centrality0.0001.0000.1060.261
Authority centrality0.0001.0000.1060.261
Information centrality-0.0010.0080.0050.004
Information centrality [Unscaled]-0.0000.000-0.0000.000
Clique membership count0.0007.0000.7411.403
Simmelian ties0.0000.0440.0060.009
Simmelian ties [Unscaled]0.0009.0001.1711.919
Clustering coefficient0.0000.5000.0860.134

Key Nodes

This chart shows the Agent that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Agent was ranked in the top three.

Total degree centrality

The Total Degree Centrality of a node is the normalized sum of its row and column degrees. Individuals or organizations who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are "in the know" are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.

Input network: Agent x Agent (size: 205, density: 0.00956666)

RankAgentValueUnscaledContext*
110.06426.0007.943
2870.04920.0005.786
3470.04418.0005.066
4550.04418.0005.066
51600.03916.0004.347
6250.03414.0003.628
7960.03414.0003.628
81230.03414.0003.628
91890.03414.0003.628
10220.02912.0002.908

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.010Mean in random network: 0.010
Std.dev: 0.010Std.dev in random network: 0.007

Back to top

In-degree centrality

The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual or a resource, the in-links are the connections that the node of interest receives from other nodes. For example, imagine an agent by knowledge matrix then the number of in-links a piece of knowledge has is the number of agents that are connected to. The scientific name of this measure is in-degree and it is calculated on the agent by agent matrices.

Input network(s): Agent x Agent

RankAgentValueUnscaled
110.06313.000
2870.04910.000
3470.0449.000
4550.0449.000
51600.0398.000
6250.0347.000
7960.0347.000
81230.0347.000
91890.0347.000
10220.0296.000

Back to top

Out-degree centrality

For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest sends to other nodes. For example, imagine an agent by knowledge matrix then the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it is calculated on the agent by agent matrices. Individuals or organizations who are high in most knowledge have more expertise or are associated with more types of knowledge than are others. If no sub-network connecting agents to knowledge exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals or organizations who are high in "most resources" have more resources or are associated with more types of resources than are others. If no sub-network connecting agents to resources exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by resource matrices.

Input network(s): Agent x Agent

RankAgentValueUnscaled
110.06313.000
2870.04910.000
3470.0449.000
4550.0449.000
51600.0398.000
6250.0347.000
7960.0347.000
81230.0347.000
91890.0347.000
10220.0296.000

Back to top

Eigenvector centrality

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Leaders of strong cliques are individuals who or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent matrices.

Input network: Agent x Agent (size: 205, density: 0.00956666)

RankAgentValueContext*
111.0003.831
2171.0003.831
3271.0003.831
4361.0003.831
5381.0003.831
6571.0003.831
7891.0003.831
8901.0003.831
91031.0003.831
101171.0003.831

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.160Mean in random network: 0.272
Std.dev: 0.312Std.dev in random network: 0.190

Back to top

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: Agent x Agent (size: 205, density: 0.00956666)

RankAgentValueUnscaledContext*
110.0110.000-1.928
2580.0110.000-1.930
31610.0110.000-1.931
41600.0110.000-1.932
51240.0110.000-1.932
61490.0110.000-1.932
71650.0110.000-1.933
81400.0110.000-1.934
91230.0110.000-1.934
101780.0110.000-1.935

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.009Mean in random network: 0.020
Std.dev: 0.003Std.dev in random network: 0.005

Back to top

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. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.

Input network: Agent x Agent (size: 205, density: 0.00956666)

RankAgentValueUnscaledContext*
110.1777323.9146.043
21650.1184885.9473.802
31490.1004149.9463.126
4580.0984075.9473.058
51230.0984065.0483.048
61780.0933861.9472.861
71600.0913782.0532.788
8470.0903717.9122.729
91610.0793270.8862.318
101240.0783212.0532.264

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.010Mean in random network: 0.018
Std.dev: 0.025Std.dev in random network: 0.026

Back to top

Hub centrality

A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.

Input network(s): Agent x Agent

RankAgentValue
111.000
2141.000
3231.000
4681.000
5891.000
6931.000
7971.000
8981.000
91311.000
101461.000

Back to top

Authority centrality

A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others. Technically, an agent is authority-central if its in-links are from agents that have are sending links to many others. The scientific name of this measure is authority centrality and it is calculated on agent by agent matrices.

Input network(s): Agent x Agent

RankAgentValue
111.000
2141.000
3231.000
4681.000
5891.000
6931.000
7971.000
8981.000
91311.000
101461.000

Back to top

Information centrality

Calculate the Stephenson and Zelen information centrality measure for each node.

Input network(s): Agent x Agent

RankAgentValue
150.008
21250.008
3710.008
41800.008
51980.008
6190.008
72040.008
81120.008
92000.008
101440.008

Back to top

Clique membership count

The number of distinct cliques to which each node belongs. Individuals or organizations who are high in number of cliques are those that belong to a large number of distinct cliques. A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. The scientific name of this measure is clique count and it is calculated on the agent by agent matrices.

Input network(s): Agent x Agent

RankAgentValue
117.000
21237.000
3226.000
4556.000
5876.000
6966.000
71896.000
8475.000
9644.000
101394.000

Back to top

Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): Agent x Agent

RankAgentValueUnscaled
110.0449.000
2870.0449.000
3550.0398.000
4960.0347.000
51600.0347.000
61890.0347.000
7220.0296.000
8250.0296.000
9470.0296.000
101230.0296.000

Back to top

Clustering coefficient

Measures the degree of clustering in a network by averaging the clustering coefficient of each node, which is defined as the density of the node's ego network.

Input network(s): Agent x Agent

RankAgentValue
180.500
2920.500
3990.500
41640.500
51570.400
61730.400
7110.333
8290.333
9310.333
10430.333

Back to top

Key Nodes Table

This shows the top scoring nodes side-by-side for selected measures.

RankBetweenness centralityCloseness centralityEigenvector centralityIn-degree centralityOut-degree centralityTotal degree centrality
1111111
21655817878787
314916127474747
45816036555555
512312438160160160
617814957252525
716016589969696
84714090123123123
9161123103189189189
10124178117222222

Produced by ORA developed at CASOS - Carnegie Mellon University