Standard Network Analysis: Knowledge x Task

Standard Network Analysis: Knowledge x Task

Input data: Knowledge x Task

Start time: Tue Oct 18 11:46:07 2011

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Network Level Measures

MeasureValue
Row count4.000
Column count5.000
Link count6.000
Density0.300

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.2500.5000.3000.100
In-degree centrality [Unscaled]1.0002.0001.2000.400
Out-degree centrality0.0000.8000.3000.300
Out-degree centrality [Unscaled]0.0004.0001.5001.500

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): Knowledge x Task

RankTaskValueUnscaled
1bombing0.5002.000
2surveillence0.2501.000
3weapon training0.2501.000
4driving training0.2501.000
5bomb prep0.2501.000

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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): Knowledge x Task

RankKnowledgeValueUnscaled
1weapons expertise0.8004.000
2religious extremism0.2001.000
3surveillance expertise0.2001.000

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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----bombing-weapons expertise-
2----surveillence-religious extremism-
3----weapon training-surveillance expertise-
4----driving training-media consultant-
5----bomb prep---