Standard Network Analysis: Agent x Organization

Standard Network Analysis: Agent x Organization

Input data: Agent x Organization

Start time: Tue Oct 18 11:55:49 2011

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Network Level Measures

MeasureValue
Row count30.000
Column count10.000
Link count34.000
Density0.113

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.4330.0650.128
In-degree centrality [Unscaled]0.00026.0003.9007.687
Out-degree centrality0.0000.2000.0650.047
Out-degree centrality [Unscaled]0.0004.0001.3000.936

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 Organization

RankOrganizationValueUnscaled
1al_qaeda0.43326.000
2Egyptian Islamic Jihad0.1177.000
3Liberation_Ar,y_for_Holy_Sites0.0674.000
4police0.0171.000
5Foundation_for_Rebirth_of_Islamic_Heritage0.0171.000

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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 Organization

RankAgentValueUnscaled
1khalfan_mohamed0.2004.000
2abdullah_ahmed_abdullah0.1503.000
3mohamed_owhali0.1503.000
4abdal_rahmad0.1002.000
5ahmed_ghailani0.1002.000
6bin_laden0.1002.000
7fahid_msalam0.1002.000
8mustafa_fadhil0.1002.000
9swedan_sheikh0.1002.000
10Mohammad_Hassan0.1002.000

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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----al_qaeda-khalfan_mohamed-
2----Egyptian Islamic Jihad-abdullah_ahmed_abdullah-
3----Liberation_Ar,y_for_Holy_Sites-mohamed_owhali-
4----police-abdal_rahmad-
5----Foundation_for_Rebirth_of_Islamic_Heritage-ahmed_ghailani-
6----united_states_of_america-bin_laden-
7----CIA-fahid_msalam-
8----Taliban-mustafa_fadhil-
9----FBI-swedan_sheikh-
10----CentGas-Mohammad_Hassan-