Standard Network Analysis: agent x task

Standard Network Analysis: agent x task

Input data: agent x task

Start time: Thu Nov 17 13:54:55 2011

Return to table of contents

Network Level Measures

MeasureValue
Row count16.000
Column count18.000
Link count30.000
Density0.104

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0210.1460.0370.036
In-degree centrality [Unscaled]1.0007.0001.7781.750
Out-degree centrality0.0000.0930.0370.033
Out-degree centrality [Unscaled]0.0005.0002.0001.768

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 task

RankTaskValueUnscaled
1defend_revenna0.1467.000
2fly_ship_to_yuWorld0.1256.000
3find_tunnel_crystals0.0633.000
4present_plan_to_sgc0.0422.000
5poison_summit0.0211.000
6infiltrate_summit0.0211.000
7poison_jaffa_at_gate0.0211.000
8create_poison0.0211.000
9replace_jerran0.0211.000
10create_memory_drug0.0211.000

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 task

RankAgentValueUnscaled
1daniel_jackson0.0935.000
2jacob_carter_selmak0.0935.000
3ren'al0.0935.000
4col_jack_o'neill0.0563.000
5teal'c0.0563.000
6lantash0.0563.000
7maj_samantha_carter0.0372.000
8aldwin0.0372.000
9lt_elliott0.0191.000
10janet_frazier0.0191.000

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----defend_revenna-daniel_jackson-
2----fly_ship_to_yuWorld-jacob_carter_selmak-
3----find_tunnel_crystals-ren'al-
4----present_plan_to_sgc-col_jack_o'neill-
5----poison_summit-teal'c-
6----infiltrate_summit-lantash-
7----poison_jaffa_at_gate-maj_samantha_carter-
8----create_poison-aldwin-
9----replace_jerran-lt_elliott-
10----create_memory_drug-janet_frazier-