Standard Network Analysis: agent---knowledge

Standard Network Analysis: agent---knowledge

Input data: agent---knowledge

Start time: Tue Oct 18 12:07:20 2011

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Network Level Measures

MeasureValue
Row count47.000
Column count32.000
Link count7.000
Density0.005

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.0210.0030.005
In-degree centrality [Unscaled]0.0002.0000.2500.500
Out-degree centrality0.0000.0310.0030.007
Out-degree centrality [Unscaled]0.0002.0000.1700.476

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---knowledge

RankKnowledgeValueUnscaled
1monitor0.0212.000
2intellig0.0111.000
3satellit0.0111.000
4videotap0.0111.000
5fbi0.0111.000
6weapons-monitor0.0111.000
7call0.0111.000

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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---knowledge

RankAgentValueUnscaled
1michael_chandler0.0312.000
2colin_powel0.0312.000
3matthew_levitt0.0161.000
4omar_sulieman0.0161.000
5imad_falouji0.0161.000
6samer_ufi0.0161.000

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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----monitor-michael_chandler-
2----intellig-colin_powel-
3----satellit-matthew_levitt-
4----videotap-omar_sulieman-
5----fbi-imad_falouji-
6----weapons-monitor-samer_ufi-
7----call-marwan_barghouti-
8----manual-muhammad_sidr-
9----televis-bernard_sabella-
10----interview-mahmoud_al-zahar-