Standard Network Analysis: resource---knowledge

Standard Network Analysis: resource---knowledge

Input data: resource---knowledge

Start time: Tue Oct 18 12:09:41 2011

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Network Level Measures

MeasureValue
Row count59.000
Column count32.000
Link count9.000
Density0.005

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.0850.0050.015
In-degree centrality [Unscaled]0.0005.0000.2810.909
Out-degree centrality0.0000.0940.0050.015
Out-degree centrality [Unscaled]0.0003.0000.1530.481

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---knowledge

RankKnowledgeValueUnscaled
1report0.0855.000
2footag0.0171.000
3video0.0171.000
4call0.0171.000
5bethlehem_university0.0171.000

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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---knowledge

RankResourceValueUnscaled
1militari0.0943.000
2sociologist0.0311.000
3support0.0311.000
4financ0.0311.000
5monei0.0311.000
6armi0.0311.000
7biologicalweapon0.0311.000

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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----report-militari-
2----footag-sociologist-
3----video-support-
4----call-financ-
5----bethlehem_university-monei-
6----manual-armi-
7----televis-biologicalweapon-
8----interview-financi-
9----monitor-missil-
10----testimoni-hospit-