Standard Network Analysis: knowledge---location

Standard Network Analysis: knowledge---location

Input data: knowledge---location

Start time: Tue Oct 18 12:08:01 2011

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Network Level Measures

MeasureValue
Row count32.000
Column count66.000
Link count11.000
Density0.005

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.1250.0050.019
In-degree centrality [Unscaled]0.0004.0000.1670.592
Out-degree centrality0.0000.0450.0050.010
Out-degree centrality [Unscaled]0.0003.0000.3440.690

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---location

RankLocationValueUnscaled
1israel0.1254.000
2bulgaria0.0632.000
3al-rafah0.0311.000
4ain_saheb0.0311.000
5terrorist_camp0.0311.000
6damascu0.0311.000
7syria0.0311.000

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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---location

RankKnowledgeValueUnscaled
1call0.0453.000
2inform0.0302.000
3intellig0.0151.000
4media0.0151.000
5identifi0.0151.000
6psycholog0.0151.000
7studi0.0151.000
8map0.0151.000

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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----israel-call-
2----bulgaria-inform-
3----al-rafah-intellig-
4----ain_saheb-media-
5----terrorist_camp-identifi-
6----damascu-psycholog-
7----syria-studi-
8----gaza_strip-map-
9----training_camp-manual-
10----iraq-televis-