Standard Network Analysis: location---agent

Standard Network Analysis: location---agent

Input data: location---agent

Start time: Tue Oct 18 12:08:24 2011

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Network Level Measures

MeasureValue
Row count66.000
Column count47.000
Link count19.000
Density0.006

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.0230.0030.005
In-degree centrality [Unscaled]0.0003.0000.4260.707
Out-degree centrality0.0000.0530.0030.008
Out-degree centrality [Unscaled]0.0005.0000.3030.778

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): location---agent

RankAgentValueUnscaled
1muhammad_horani0.0233.000
2sirhan_sirhan0.0152.000
3hassan_moayad0.0152.000
4shaul_mofaz0.0152.000
5mokled_humaid0.0081.000
6yasser_arafat0.0081.000
7raanan_gissin0.0081.000
8ismail_abu_shanab0.0081.000
9georg_bush0.0081.000
10michael_chandler0.0081.000

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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): location---agent

RankLocationValueUnscaled
1israel0.0535.000
2iraq0.0323.000
3gaza_strip0.0111.000
4italy0.0111.000
5exil0.0111.000
6gaza_city0.0111.000
7lebanon0.0111.000
8tulkarm0.0111.000
9jerusalem0.0111.000
10yemen0.0111.000

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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----muhammad_horani-israel-
2----sirhan_sirhan-iraq-
3----hassan_moayad-gaza_strip-
4----shaul_mofaz-italy-
5----mokled_humaid-exil-
6----yasser_arafat-gaza_city-
7----raanan_gissin-lebanon-
8----ismail_abu_shanab-tulkarm-
9----georg_bush-jerusalem-
10----michael_chandler-yemen-