Standard Network Analysis: Resource x Task

Standard Network Analysis: Resource x Task

Input data: Resource x Task

Start time: Tue Oct 18 11:56:46 2011

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Network Level Measures

MeasureValue
Row count18.000
Column count37.000
Link count35.000
Density0.053

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.1940.0290.043
In-degree centrality [Unscaled]0.0007.0001.0271.551
Out-degree centrality0.0000.0950.0290.031
Out-degree centrality [Unscaled]0.0007.0002.1112.258

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): Resource x Task

RankTaskValueUnscaled
1bomb_preparation0.1947.000
2bombing0.1676.000
3conceal_bomb_in_car0.0562.000
4driving_training0.0562.000
5get_money0.0562.000
6leave_bomb_and_car0.0562.000
7purchase_acetylene0.0562.000
8purchase_oxygen0.0562.000
9purchase_vehicle0.0562.000
10run_bomb_factory0.0562.000

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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): Resource x Task

RankResourceValueUnscaled
1bomb0.0957.000
2vehicle0.0957.000
3money0.0816.000
4bomb_material0.0544.000
5acetylene0.0272.000
6bomb_factory0.0272.000
7oxygen0.0272.000
8residence0.0272.000
9bomber0.0141.000
10charity0.0141.000

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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_preparation-bomb-
2----bombing-vehicle-
3----conceal_bomb_in_car-money-
4----driving_training-bomb_material-
5----get_money-acetylene-
6----leave_bomb_and_car-bomb_factory-
7----purchase_acetylene-oxygen-
8----purchase_oxygen-residence-
9----purchase_vehicle-bomber-
10----run_bomb_factory-charity-