Standard Network Analysis: event x resource

Standard Network Analysis: event x resource

Input data: event x resource

Start time: Thu Nov 17 13:53:28 2011

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Network Level Measures

MeasureValue
Row count14.000
Column count7.000
Link count8.000
Density0.082

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0000.1430.0650.052
In-degree centrality [Unscaled]0.00010.0004.5713.620
Out-degree centrality0.0000.3710.0650.115
Out-degree centrality [Unscaled]0.00013.0002.2864.025

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 resource

RankResourceValueUnscaled
1symbiote_poison0.14310.000
2memory_altering_drug0.14310.000
3data_crystal0.0574.000
4cargo_ship0.0433.000
5tok'ra_tunneling_crystals0.0433.000
6tok'ra_communicator0.0292.000

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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 resource

RankEventValueUnscaled
1summit_meeting0.37113.000
2revanna_bombardment0.2579.000
3gate_attack0.1435.000
4replace_jarren0.1435.000

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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----symbiote_poison-summit_meeting-
2----memory_altering_drug-revanna_bombardment-
3----data_crystal-gate_attack-
4----cargo_ship-replace_jarren-
5----tok'ra_tunneling_crystals-revanna_meeting-
6----tok'ra_communicator-sgc_meeting-
7----reole_chameleon-tollana_attack-
8------escape_tunnels-
9------bring_crystals_to_sgc-
10------osiris_arrives-