STANDARD NETWORK ANALYSIS REPORT

STANDARD NETWORK ANALYSIS REPORT

Input data: dolphins

Start time: Mon Oct 17 12:39:26 2011

Data Description

Calculates common social network measures on each selected input network.

Network agent x agent

Network Level Measures

MeasureValue
Row count62.000
Column count62.000
Link count159.000
Density0.084
Components of 1 node (isolates)0
Components of 2 nodes (dyadic isolates)0
Components of 3 or more nodes1
Reciprocity1.000
Characteristic path length3.357
Clustering coefficient0.259
Network levels (diameter)8.000
Network fragmentation0.000
Krackhardt connectedness1.000
Krackhardt efficiency0.946
Krackhardt hierarchy0.000
Krackhardt upperboundedness1.000
Degree centralization0.116
Betweenness centralization0.212
Closeness centralization0.227
Eigenvector centralization0.329
Reciprocal (symmetric)?Yes

Node Level Measures

MeasureMinMaxAvgStddev
Total degree centrality0.0160.1970.0840.048
Total degree centrality [Unscaled]1.00012.0005.1292.932
In-degree centrality0.0160.1970.0840.048
In-degree centrality [Unscaled]1.00012.0005.1292.932
Out-degree centrality0.0160.1970.0840.048
Out-degree centrality [Unscaled]1.00012.0005.1292.932
Eigenvector centrality0.0010.4470.1280.126
Eigenvector centrality [Unscaled]0.0010.3160.0910.089
Eigenvector centrality per component0.0010.3160.0910.089
Closeness centrality0.1780.4180.3070.052
Closeness centrality [Unscaled]0.0030.0070.0050.001
In-Closeness centrality0.1780.4180.3070.052
In-Closeness centrality [Unscaled]0.0030.0070.0050.001
Betweenness centrality0.0000.2480.0390.051
Betweenness centrality [Unscaled]0.000454.27471.88792.510
Hub centrality0.0010.4470.1280.126
Authority centrality0.0010.4470.1280.126
Information centrality0.0060.0220.0160.004
Information centrality [Unscaled]0.4491.5881.1470.317
Clique membership count0.00010.0002.5322.360
Simmelian ties0.0000.1800.0640.049
Simmelian ties [Unscaled]0.00011.0003.9033.009
Clustering coefficient0.0000.6670.2590.197

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: 62, density: 0.0840825)

RankAgentValueUnscaledContext*
1Grin0.19712.0003.196
2SN40.18011.0002.731
3Topless0.18011.0002.731
4Scabs0.16410.0002.266
5Trigger0.16410.0002.266
6Jet0.1489.0001.801
7Kringel0.1489.0001.801
8Patchback0.1489.0001.801
9Web0.1489.0001.801
10Beescratch0.1318.0001.335

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

Mean: 0.084Mean in random network: 0.084
Std.dev: 0.048Std.dev in random network: 0.035

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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): agent x agent

RankAgentValueUnscaled
1Grin0.19712.000
2SN40.18011.000
3Topless0.18011.000
4Scabs0.16410.000
5Trigger0.16410.000
6Jet0.1489.000
7Kringel0.1489.000
8Patchback0.1489.000
9Web0.1489.000
10Beescratch0.1318.000

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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): agent x agent

RankAgentValueUnscaled
1Grin0.19712.000
2SN40.18011.000
3Topless0.18011.000
4Scabs0.16410.000
5Trigger0.16410.000
6Jet0.1489.000
7Kringel0.1489.000
8Patchback0.1489.000
9Web0.1489.000
10Beescratch0.1318.000

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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: agent x agent (size: 62, density: 0.0840825)

RankAgentValueUnscaledContext*
1Grin0.4470.316-0.047
2SN40.4250.301-0.134
3Topless0.4030.285-0.223
4Scabs0.3980.281-0.245
5TR990.3080.218-0.606
6Patchback0.2990.212-0.640
7Trigger0.2980.211-0.646
8Hook0.2940.208-0.661
9SN90.2940.208-0.662
10MN1050.2930.207-0.665

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

Mean: 0.128Mean in random network: 0.458
Std.dev: 0.126Std.dev in random network: 0.248

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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): agent x agent

RankAgentValue
1Grin0.316
2SN40.301
3Topless0.285
4Scabs0.281
5TR990.218
6Patchback0.212
7Trigger0.211
8Hook0.208
9SN90.208
10MN1050.207

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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: agent x agent (size: 62, density: 0.0840825)

RankAgentValueUnscaledContext*
1SN1000.4180.0071.718
2SN90.4040.0071.438
3SN40.3990.0071.332
4Kringel0.3910.0061.177
5Grin0.3770.0060.884
6Beescratch0.3720.0060.792
7DN630.3650.0060.657
8Oscar0.3650.0060.657
9Scabs0.3650.0060.657
10Double0.3630.0060.613

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

Mean: 0.307Mean in random network: 0.333
Std.dev: 0.052Std.dev in random network: 0.050

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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): agent x agent

RankAgentValueUnscaled
1SN1000.4180.007
2SN90.4040.007
3SN40.3990.007
4Kringel0.3910.006
5Grin0.3770.006
6Beescratch0.3720.006
7DN630.3650.006
8Oscar0.3650.006
9Scabs0.3650.006
10Double0.3630.006

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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: agent x agent (size: 62, density: 0.0840825)

RankAgentValueUnscaledContext*
1SN1000.248454.2742.330
2Beescratch0.213390.3841.955
3SN90.143261.9641.200
4SN40.139253.5831.151
5DN630.118216.3770.933
6Jet0.114209.1690.890
7Kringel0.103187.8420.765
8Upbang0.099181.3930.727
9Trigger0.085154.9590.572
10Web0.084154.0950.567

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

Mean: 0.039Mean in random network: 0.031
Std.dev: 0.051Std.dev in random network: 0.093

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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): agent x agent

RankAgentValue
1Grin0.447
2SN40.425
3Topless0.403
4Scabs0.398
5TR990.308
6Patchback0.299
7Trigger0.298
8Hook0.294
9SN90.294
10MN1050.293

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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): agent x agent

RankAgentValue
1Grin0.447
2SN40.425
3Topless0.403
4Scabs0.398
5TR990.308
6Patchback0.299
7Trigger0.298
8Hook0.294
9SN90.294
10MN1050.293

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Information centrality

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

Input network(s): agent x agent

RankAgentValueUnscaled
1Grin0.0221.588
2SN40.0221.584
3Topless0.0221.551
4Kringel0.0221.547
5SN90.0221.534
6Scabs0.0211.516
7SN1000.0211.504
8Patchback0.0211.473
9TR990.0201.457
10Trigger0.0201.447

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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): agent x agent

RankAgentValue
1Grin10.000
2Topless9.000
3Scabs8.000
4SN48.000
5Gallatin6.000
6Hook5.000
7Kringel5.000
8SN635.000
9SN95.000
10Upbang5.000

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Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): agent x agent

RankAgentValueUnscaled
1Topless0.18011.000
2Grin0.16410.000
3SN40.16410.000
4Scabs0.1489.000
5Gallatin0.1318.000
6Feather0.1157.000
7Jet0.1157.000
8Kringel0.1157.000
9SN630.1157.000
10SN90.1157.000

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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): agent x agent

RankAgentValue
1Mus0.667
2Notch0.667
3Hook0.600
4SN900.600
5DN210.533
6MN1050.533
7MN830.533
8Feather0.524
9Jonah0.524
10DN160.500

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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
1SN100SN100GrinGrinGrinSN100GrinGrin
2BeescratchSN9SN4SN4SN4SN9SN4SN4
3SN9SN4ToplessToplessToplessSN4ToplessTopless
4SN4KringelScabsScabsScabsKringelScabsScabs
5DN63GrinTR99TR99TriggerGrinTriggerTrigger
6JetBeescratchPatchbackPatchbackJetBeescratchJetJet
7KringelDN63TriggerTriggerKringelDN63KringelKringel
8UpbangOscarHookHookPatchbackOscarPatchbackPatchback
9TriggerScabsSN9SN9WebScabsWebWeb
10WebDoubleMN105MN105BeescratchDoubleBeescratchBeescratch

Produced by ORA developed at CASOS - Carnegie Mellon University