Standard Network Analysis: ACTOR#2

Standard Network Analysis: ACTOR#2

Input data: ACTOR#2

Start time: Tue Oct 18 15:31:14 2011

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Network Level Measures

MeasureValue
Row count21.000
Column count21.000
Link count21.000
Density0.050
Components of 1 node (isolates)7
Components of 2 nodes (dyadic isolates)0
Components of 3 or more nodes2
Reciprocity0.500
Characteristic path length2.452
Clustering coefficient0.058
Network levels (diameter)5.000
Network fragmentation0.724
Krackhardt connectedness0.276
Krackhardt efficiency0.957
Krackhardt hierarchy0.500
Krackhardt upperboundedness0.957
Degree centralization0.166
Betweenness centralization0.093
Closeness centralization0.043
Eigenvector centralization0.660
Reciprocal (symmetric)?No (50% of the links are reciprocal)

Node Level Measures

MeasureMinMaxAvgStddev
Total degree centrality0.0000.2000.0500.056
Total degree centrality [Unscaled]0.0008.0002.0002.247
In-degree centrality0.0000.2500.0500.069
In-degree centrality [Unscaled]0.0005.0001.0001.380
Out-degree centrality0.0000.1500.0500.049
Out-degree centrality [Unscaled]0.0003.0001.0000.976
Eigenvector centrality0.0000.7930.1950.239
Eigenvector centrality [Unscaled]0.0000.5610.1380.169
Eigenvector centrality per component0.0000.2940.0840.084
Closeness centrality0.0480.0800.0600.011
Closeness centrality [Unscaled]0.0020.0040.0030.001
In-Closeness centrality0.0480.0880.0620.018
In-Closeness centrality [Unscaled]0.0020.0040.0030.001
Betweenness centrality0.0000.1040.0150.031
Betweenness centrality [Unscaled]0.00039.5005.81011.913
Hub centrality0.0000.7090.1960.238
Authority centrality0.0001.0990.1510.269
Information centrality0.0000.1070.0480.040
Information centrality [Unscaled]0.0000.9780.4370.366
Clique membership count0.0001.0000.1430.350
Simmelian ties0.0000.0000.0000.000
Simmelian ties [Unscaled]0.0000.0000.0000.000
Clustering coefficient0.0001.0000.0580.214

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: ACTOR#2 (size: 21, density: 0.05)

RankAgentValueUnscaledContext*
120.2008.0003.154
210.1757.0002.628
340.1255.0001.577
4120.1004.0001.051
5180.0753.0000.526
630.0502.0000.000
750.0502.0000.000
880.0502.0000.000
9160.0502.0000.000
10190.0502.0000.000

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

Mean: 0.050Mean in random network: 0.050
Std.dev: 0.056Std.dev in random network: 0.048

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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): ACTOR#2

RankAgentValueUnscaled
120.2505.000
210.2004.000
340.1503.000
4120.1002.000
530.0501.000
680.0501.000
7140.0501.000
8160.0501.000
9180.0501.000
10190.0501.000

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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): ACTOR#2

RankAgentValueUnscaled
110.1503.000
220.1503.000
340.1002.000
450.1002.000
5120.1002.000
6180.1002.000
730.0501.000
860.0501.000
980.0501.000
10130.0501.000

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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: ACTOR#2 (size: 21, density: 0.05)

RankAgentValueUnscaledContext*
120.7930.5611.749
210.7000.4951.462
3180.5420.3830.976
450.3670.2590.438
5120.3380.2390.350
660.2880.2040.195
7210.2880.2040.195
8160.2540.1800.091
940.2330.1640.024
10190.2180.154-0.021

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

Mean: 0.195Mean in random network: 0.225
Std.dev: 0.239Std.dev in random network: 0.325

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Eigenvector centrality per component

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Each component is extracted as a separate network, Eigenvector Centrality is computed on it and scaled according to the component size. The scores are then combined into a single result vector.

Input network(s): ACTOR#2

RankAgentValue
120.294
210.259
3180.201
450.136
5120.125
660.107
7210.107
830.101
9160.094
1040.086

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Closeness centrality

The average closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the average distance in the network from the node to all other nodes.

Input network: ACTOR#2 (size: 21, density: 0.05)

RankAgentValueUnscaledContext*
150.0800.004-1.468
260.0730.004-1.935
3190.0720.004-2.005
410.0700.004-2.123
520.0700.003-2.156
6120.0700.003-2.156
7180.0690.003-2.172
840.0690.003-2.220
9160.0690.003-2.220
10210.0680.003-2.251

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

Mean: 0.060Mean in random network: 0.102
Std.dev: 0.011Std.dev in random network: 0.015

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In-Closeness centrality

The average closeness of a node from the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network to the node and from all other nodes.

Input network(s): ACTOR#2

RankAgentValueUnscaled
110.0880.004
220.0870.004
3120.0870.004
440.0850.004
5160.0840.004
6180.0840.004
7210.0840.004
880.0820.004
9140.0520.003
1030.0500.002

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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: ACTOR#2 (size: 21, density: 0.05)

RankAgentValueUnscaledContext*
110.10439.500-0.049
220.08030.500-0.122
3120.07127.000-0.150
440.05721.500-0.195
5190.0072.500-0.349
630.0031.000-0.361

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

Mean: 0.015Mean in random network: 0.120
Std.dev: 0.031Std.dev in random network: 0.325

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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): ACTOR#2

RankAgentValue
1180.709
210.672
350.518
460.436
5210.436
620.399
7120.355
8160.273
940.154
1080.081

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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): ACTOR#2

RankAgentValue
121.099
210.689
3120.328
4160.267
540.205
6190.205
7180.158
8210.158
980.061
1030.000

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Information centrality

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

Input network(s): ACTOR#2

RankAgentValueUnscaled
150.1070.978
210.1010.924
320.0930.856
4180.0930.853
5120.0890.818
6190.0760.698
760.0740.677
8130.0700.638
930.0700.638
1040.0680.623

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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): ACTOR#2

RankAgentValue
111.000
221.000
3181.000

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Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): ACTOR#2

RankAgentValueUnscaled
1All nodes have this value0.000

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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): ACTOR#2

RankAgentValue
1181.000
210.167
320.050

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Key Nodes Table

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

RankBetweenness centralityCloseness centralityEigenvector centralityEigenvector centrality per componentIn-degree centralityIn-Closeness centralityOut-degree centralityTotal degree centrality
115222112
226111221
31219181841244
44155124512
519212123161218
631266818183
75182121142135
86416316868
97164161814816
108211941931319