Standard Network Analysis: ACTOR#19

Standard Network Analysis: ACTOR#19

Input data: ACTOR#19

Start time: Tue Oct 18 15:31:08 2011

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Network Level Measures

MeasureValue
Row count21.000
Column count21.000
Link count74.000
Density0.176
Components of 1 node (isolates)1
Components of 2 nodes (dyadic isolates)0
Components of 3 or more nodes1
Reciprocity0.451
Characteristic path length2.211
Clustering coefficient0.466
Network levels (diameter)5.000
Network fragmentation0.095
Krackhardt connectedness0.905
Krackhardt efficiency0.813
Krackhardt hierarchy0.362
Krackhardt upperboundedness0.994
Degree centralization0.386
Betweenness centralization0.220
Closeness centralization0.161
Eigenvector centralization0.347
Reciprocal (symmetric)?No (45% of the links are reciprocal)

Node Level Measures

MeasureMinMaxAvgStddev
Total degree centrality0.0000.5250.1760.124
Total degree centrality [Unscaled]0.00021.0007.0484.952
In-degree centrality0.0000.5500.1760.149
In-degree centrality [Unscaled]0.00011.0003.5242.986
Out-degree centrality0.0000.5000.1760.123
Out-degree centrality [Unscaled]0.00010.0003.5242.461
Eigenvector centrality0.0000.5840.2700.149
Eigenvector centrality [Unscaled]0.0000.4130.1910.105
Eigenvector centrality per component0.0000.3930.1820.100
Closeness centrality0.0480.2560.1820.057
Closeness centrality [Unscaled]0.0020.0130.0090.003
In-Closeness centrality0.0480.2300.1820.057
In-Closeness centrality [Unscaled]0.0020.0110.0090.003
Betweenness centrality0.0000.2560.0470.074
Betweenness centrality [Unscaled]0.00097.21217.76228.059
Hub centrality0.0000.6590.2530.176
Authority centrality0.0000.6900.2350.200
Information centrality0.0000.0810.0480.022
Information centrality [Unscaled]0.0002.7621.6150.759
Clique membership count0.0008.0002.4762.084
Simmelian ties0.0000.5000.1000.123
Simmelian ties [Unscaled]0.00010.0002.0002.469
Clustering coefficient0.0001.0000.4660.291

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#19 (size: 21, density: 0.17619)

RankAgentValueUnscaledContext*
1140.52521.0004.196
2190.40016.0002.692
320.32513.0001.790
4110.30012.0001.489
550.2008.0000.286
670.2008.0000.286
7120.2008.0000.286
8210.2008.0000.286
910.1757.000-0.014
1030.1757.000-0.014

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

Mean: 0.176Mean in random network: 0.176
Std.dev: 0.124Std.dev in random network: 0.083

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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#19

RankAgentValueUnscaled
1140.55011.000
220.50010.000
3190.3507.000
4110.3006.000
550.2505.000
670.2505.000
710.2004.000
830.2004.000
9210.2004.000
1040.1503.000

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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#19

RankAgentValueUnscaled
1140.50010.000
2190.4509.000
3110.3006.000
4120.2505.000
5130.2004.000
6150.2004.000
7170.2004.000
8180.2004.000
9210.2004.000
1010.1503.000

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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#19 (size: 21, density: 0.17619)

RankAgentValueUnscaledContext*
1140.5840.4130.433
2190.5580.3950.349
320.4880.3450.116
4110.3900.276-0.205
5120.3300.233-0.404
650.3240.229-0.422
7210.2990.211-0.506
870.2910.206-0.531
930.2840.201-0.556
10150.2840.201-0.556

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

Mean: 0.270Mean in random network: 0.453
Std.dev: 0.149Std.dev in random network: 0.303

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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#19

RankAgentValue
1140.393
2190.376
320.328
4110.263
5120.222
650.218
7210.201
870.196
930.191
10150.191

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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#19 (size: 21, density: 0.17619)

RankAgentValueUnscaledContext*
1130.2560.013-2.528
2190.2270.011-3.038
3140.2200.011-3.169
4110.2110.011-3.330
5210.2110.011-3.330
6150.2060.010-3.406
730.2040.010-3.443
850.2040.010-3.443
990.2040.010-3.443
10120.2000.010-3.514

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

Mean: 0.182Mean in random network: 0.401
Std.dev: 0.057Std.dev in random network: 0.057

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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#19

RankAgentValueUnscaled
120.2300.011
2140.2300.011
340.2270.011
470.2150.011
5200.2150.011
6210.2130.011
7110.2080.010
8190.2080.010
910.2060.010
1050.2040.010

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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#19 (size: 21, density: 0.17619)

RankAgentValueUnscaledContext*
1140.25697.2123.537
2190.19774.9382.386
320.15057.1601.468
4120.12647.7980.984
510.09636.4520.398
6210.07227.312-0.074
7110.05520.719-0.415
860.0114.250-1.266
970.0113.993-1.279
1050.0041.333-1.416

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

Mean: 0.047Mean in random network: 0.076
Std.dev: 0.074Std.dev in random network: 0.051

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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#19

RankAgentValue
1140.659
2190.601
3110.453
4180.369
5150.364
6130.332
7210.326
830.295
990.290
1070.274

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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#19

RankAgentValue
1140.690
220.557
3190.481
4110.449
550.425
630.378
770.334
8150.312
9210.257
1090.226

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Information centrality

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

Input network(s): ACTOR#19

RankAgentValueUnscaled
1140.0812.762
2190.0802.717
3110.0672.279
4120.0662.239
5130.0602.043
6180.0592.004
7170.0591.996
8150.0571.931
9210.0561.899
1020.0521.776

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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#19

RankAgentValue
128.000
2147.000
3195.000
414.000
5124.000
673.000
7113.000
8213.000
942.000
1052.000

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Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): ACTOR#19

RankAgentValueUnscaled
1140.50010.000
2110.2505.000
3190.2505.000
420.1503.000
530.1503.000
650.1503.000
770.1503.000
8150.1503.000
9210.1503.000
1090.1002.000

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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#19

RankAgentValue
131.000
2151.000
3130.917
490.750
5170.667
6180.667
750.600
870.550
9210.550
1080.500

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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
1141314141421414
2191919192141919
321422194112
4121111111171211
51211212520135
6211555721157
711321211111712
865773191821
97933211211
1051215154513