Standard Network Analysis: task x location

Standard Network Analysis: task x location

Input data: task x location

Start time: Tue Oct 18 11:58:26 2011

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Network Level Measures

MeasureValue
Row count25.000
Column count5.000
Link count39.000
Density0.312

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.6400.3120.260
In-degree centrality [Unscaled]0.00016.0007.8006.493
Out-degree centrality0.0000.4000.3120.114
Out-degree centrality [Unscaled]0.0002.0001.5600.571

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): task x location

RankLocationValueUnscaled
1kenya0.64016.000
2tanzania0.60015.000
3somalia0.2005.000
4afghanistan0.1203.000

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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): task x location

RankTaskValueUnscaled
1load_bomb0.4002.000
2brief_attack_team0.4002.000
3final_reconnaissance_mission0.4002.000
4lead_attackers_to_embassy0.4002.000
5film_videotape_announcing_martyrdom0.4002.000
6driving_training0.4002.000
7bomb_preparation0.4002.000
8driving0.4002.000
9conceal_bomb_in_car0.4002.000
10leave_bomb_and_car0.4002.000

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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----kenya-load_bomb-
2----tanzania-brief_attack_team-
3----somalia-final_reconnaissance_mission-
4----afghanistan-lead_attackers_to_embassy-
5----pakistan-film_videotape_announcing_martyrdom-
6------driving_training-
7------bomb_preparation-
8------driving-
9------conceal_bomb_in_car-
10------leave_bomb_and_car-