Standard Network Analysis: agent x location

Standard Network Analysis: agent x location

Input data: agent x location

Start time: Tue Oct 18 11:57:41 2011

Return to table of contents

Network Level Measures

MeasureValue
Row count18.000
Column count5.000
Link count18.000
Density0.200

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.4440.2000.143
In-degree centrality [Unscaled]0.0008.0003.6002.577
Out-degree centrality0.2000.2000.2000.000
Out-degree centrality [Unscaled]1.0001.0001.0000.000

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 x location

RankLocationValueUnscaled
1kenya0.4448.000
2tanzania0.2224.000
3somalia0.1673.000
4afghanistan0.1673.000

Back to top

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 x location

RankAgentValueUnscaled
1All nodes have this value0.200

Back to top

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-Muhammed Atef-
2----tanzania-Fazul Abdullah Mohammed-
3----somalia-Abdullah Ahmed Abdullah-
4----afghanistan-Khalfan Khamis Mohamed-
5----pakistan-Ahmed the German-
6------Ahmed Khalfan Ghalilani-
7------Sheik Ahmed Salim Swedan-
8------Fahid Mohammed Ally Msalam-
9------Azzam-
10------Mustafa Mohamed Fadhil-