Standard Network Analysis: Agent x Resource

Standard Network Analysis: Agent x Resource

Input data: Agent x Resource

Start time: Tue Oct 18 11:55:54 2011

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Network Level Measures

MeasureValue
Row count30.000
Column count18.000
Link count43.000
Density0.080

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.2670.0800.071
In-degree centrality [Unscaled]0.0008.0002.3892.138
Out-degree centrality0.0000.5000.0800.125
Out-degree centrality [Unscaled]0.0009.0001.4332.246

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
1bomb0.2678.000
2bomb_factory0.1675.000
3bomb_material0.1675.000
4money0.1675.000
5vehicle0.1334.000
6telephone0.1003.000
7acetylene0.0672.000
8nuclear0.0672.000
9oxygen0.0672.000
10residence0.0672.000

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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_laden0.5009.000
2khalfan_mohamed0.3336.000
3ahmed_ghailani0.2785.000
4abdal_rahmad0.2224.000
5abdullah_ahmed_abdullah0.2224.000
6fazul_mohammed0.1673.000
7jamal_al-fadl0.1673.000
8mohamed_owhali0.1673.000
9wadih_el-hage0.1673.000
10mohammed_odeh0.0561.000

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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----bomb_factory-khalfan_mohamed-
3----bomb_material-ahmed_ghailani-
4----money-abdal_rahmad-
5----vehicle-abdullah_ahmed_abdullah-
6----telephone-fazul_mohammed-
7----acetylene-jamal_al-fadl-
8----nuclear-mohamed_owhali-
9----oxygen-wadih_el-hage-
10----residence-mohammed_odeh-