Standard Network Analysis: event x location

Standard Network Analysis: event x location

Input data: event x location

Start time: Tue Oct 18 11:49:22 2011

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Network Level Measures

MeasureValue
Row count5.000
Column count29.000
Link count18.000
Density0.124

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.2000.0660.066
In-degree centrality [Unscaled]0.0002.0000.6550.658
Out-degree centrality0.0170.1900.0660.064
Out-degree centrality [Unscaled]1.00011.0003.8003.709

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): event x location

RankLocationValueUnscaled
1saudi_arabia0.2002.000
2usa0.2002.000
3africa0.2002.000
4tanzania0.1001.000
5nairobi0.1001.000
6kenya0.1001.000
7london0.1001.000
8pakistan0.1001.000
9farm0.1001.000
10indonesia0.1001.000

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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): event x location

RankEventValueUnscaled
1jihad0.19011.000
2tanzania_embassy_bombing_19980.0523.000
3kenya_embassy_bombing_19980.0523.000
4wtc0.0171.000
5khobar_towers_bombing_19960.0171.000

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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----saudi_arabia-jihad-
2----usa-tanzania_embassy_bombing_1998-
3----africa-kenya_embassy_bombing_1998-
4----tanzania-wtc-
5----nairobi-khobar_towers_bombing_1996-
6----kenya---
7----london---
8----pakistan---
9----farm---
10----indonesia---