Standard Network Analysis: Agent x Knowledge

Standard Network Analysis: Agent x Knowledge

Input data: Agent x Knowledge

Start time: Tue Oct 18 11:53:08 2011

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Network Level Measures

MeasureValue
Row count21.000
Column count24.000
Link count50.000
Density0.099

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.2380.0990.064
In-degree centrality [Unscaled]0.0005.0002.0831.351
Out-degree centrality0.0000.7500.0990.168
Out-degree centrality [Unscaled]0.00018.0002.3814.029

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 Knowledge

RankKnowledgeValueUnscaled
1religious_extremism0.2385.000
2surveillance_expertise0.2385.000
3weapons_expertise0.1904.000
4secular0.1904.000
5married0.1904.000
6document0.1433.000
7media_consultant0.0952.000
8photograph0.0952.000
9intelligence0.0952.000
10religious0.0952.000

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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 Knowledge

RankAgentValueUnscaled
1bin_laden0.75018.000
2ahmed_ghailani0.2506.000
3khalid_al-fawwaz0.2506.000
4jamal_al-fadl0.2085.000
5mahmud_abouhalima0.1674.000
6fazul_mohammed0.1253.000
7ali_mohamed0.0832.000
8mohammed_salim0.0832.000
9jihad_mohammed_ali0.0832.000
10abdullah_ahmed_abdullah0.0421.000

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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----religious_extremism-bin_laden-
2----surveillance_expertise-ahmed_ghailani-
3----weapons_expertise-khalid_al-fawwaz-
4----secular-jamal_al-fadl-
5----married-mahmud_abouhalima-
6----document-fazul_mohammed-
7----media_consultant-ali_mohamed-
8----photograph-mohammed_salim-
9----intelligence-jihad_mohammed_ali-
10----religious-abdullah_ahmed_abdullah-