Standard Network Analysis: agent x knowledge

Standard Network Analysis: agent x knowledge

Input data: agent x knowledge

Start time: Thu Nov 17 13:53:04 2011

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Network Level Measures

MeasureValue
Row count5.000
Column count9.000
Link count10.000
Density0.222

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.2330.1040.099
In-degree centrality [Unscaled]0.0003.5001.5561.480
Out-degree centrality0.0370.2590.1040.082
Out-degree centrality [Unscaled]1.0007.0002.8002.205

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 knowledge

RankKnowledgeValueUnscaled
1pilot0.2333.500
2military_command0.2333.500
3speak_gou'ald0.2003.000
4spying0.1332.000
5tok'ra_tech0.1332.000

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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 x knowledge

RankAgentValueUnscaled
1daniel_jackson0.2597.000
2col_jack_o'neill0.0932.500
3maj_samantha_carter0.0932.500
4teal'c0.0371.000
5gen_hammond0.0371.000

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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----pilot-daniel_jackson-
2----military_command-col_jack_o'neill-
3----speak_gou'ald-maj_samantha_carter-
4----spying-teal'c-
5----tok'ra_tech-gen_hammond-
6----symbiote_poison_formula---
7----memory_drug_formula---
8----crystal_tunneling---
9----ring_room_location---