Standard Network Analysis: Agent x Task

Standard Network Analysis: Agent x Task

Input data: Agent x Task

Start time: Tue Oct 18 11:51:15 2011

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Network Level Measures

MeasureValue
Row count26.000
Column count43.000
Link count108.000
Density0.097

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.1670.0350.038
In-degree centrality [Unscaled]0.00013.0002.6982.953
Out-degree centrality0.0000.1780.0350.046
Out-degree centrality [Unscaled]0.00023.0004.4625.878

Key Nodes

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 Task

RankTaskValueUnscaled
1arrest0.16713.000
2weapon_training0.1159.000
3bomb_preparation0.1038.000
4bombing0.1038.000
5trial0.1038.000
6attack0.0776.000
7convicted0.0776.000
8imprison0.0776.000
9accusation0.0645.000
10indictment0.0645.000

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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 Task

RankAgentValueUnscaled
1bin_laden0.17823.000
2khalfan_mohamed0.14018.000
3ahmed_ghailani0.10113.000
4mohammed_odeh0.09312.000
5mohamed_owhali0.0709.000
6fazul_mohammed0.0476.000
7wadih_el-hage0.0395.000
8ali_mohamed0.0395.000
9jamal_al-fadl0.0314.000
10abdullah_ahmed_abdullah0.0314.000

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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
1----arrest-bin_laden-
2----weapon_training-khalfan_mohamed-
3----bomb_preparation-ahmed_ghailani-
4----bombing-mohammed_odeh-
5----trial-mohamed_owhali-
6----attack-fazul_mohammed-
7----convicted-wadih_el-hage-
8----imprison-ali_mohamed-
9----accusation-jamal_al-fadl-
10----indictment-abdullah_ahmed_abdullah-