Standard Network Analysis: Agent x Resource

Standard Network Analysis: Agent x Resource

Input data: Agent x Resource

Start time: Tue Oct 18 11:53:24 2011

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Network Level Measures

MeasureValue
Row count21.000
Column count21.000
Link count34.000
Density0.077

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.2380.0770.054
In-degree centrality [Unscaled]0.0005.0001.6191.133
Out-degree centrality0.0000.7140.0770.159
Out-degree centrality [Unscaled]0.00015.0001.6193.331

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 Resource

RankResourceValueUnscaled
1bomb0.2385.000
2money0.1433.000
3bomb_material0.1433.000
4vehicle0.1433.000
5bomb_factory0.1433.000
6nuclear0.0952.000
7contacts0.0952.000
8telephone0.0481.000
9ship0.0481.000
10missile0.0481.000

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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 Resource

RankAgentValueUnscaled
1bin_laden0.71415.000
2ahmed_ghailani0.2385.000
3abdullah_ahmed_abdullah0.1904.000
4fazul_mohammed0.1433.000
5jamal_al-fadl0.1433.000
6mohammed_salim0.0481.000
7mahmud_abouhalima0.0481.000
8abdal_rahmad0.0481.000
9mustafa_fadhil0.0481.000

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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----bomb-bin_laden-
2----money-ahmed_ghailani-
3----bomb_material-abdullah_ahmed_abdullah-
4----vehicle-fazul_mohammed-
5----bomb_factory-jamal_al-fadl-
6----nuclear-mohammed_salim-
7----contacts-mahmud_abouhalima-
8----telephone-abdal_rahmad-
9----ship-mustafa_fadhil-
10----missile-ahmed_the_german-