Standard Network Analysis: agent x knowledge

Standard Network Analysis: agent x knowledge

Input data: agent x knowledge

Start time: Fri Oct 14 14:51:52 2011

Return to table of contents

Network Level Measures

MeasureValue
Row count16.000
Column count19.000
Link count82.000
Density0.270

Node Level Measures

MeasureMinMaxAvgStddev
In-degree centrality0.0630.6880.2700.172
In-degree centrality [Unscaled]1.00011.0004.3162.754
Out-degree centrality0.1050.5260.2700.123
Out-degree centrality [Unscaled]2.00010.0005.1252.342

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
1Web Development (HTML)0.68811.000
2Unix/Java/C++ Programming0.5639.000
3ATG Dynamo Platform0.5008.000
4Content Design and Development0.4387.000
5Software Engineering Experience0.3756.000
6Application Architecture Design0.3135.000
7Screen Design0.3135.000
8SQL/Oracle Database Programming0.3135.000
9Interwoven Platform0.2504.000
10Interface Design/Development0.2504.000

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 knowledge

RankAgentValueUnscaled
1Technical Lead0.52610.000
2Art Director0.4749.000
3Project Manager0.4218.000
4Data Architect0.3687.000
5Design Lead0.3166.000
6Interactive Lead0.3166.000
7Application Architect0.3166.000
8Designer0.2635.000
9Business Analyst 10.2114.000
10Software Engineer 10.2114.000

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----Web Development (HTML)-Technical Lead-
2----Unix/Java/C++ Programming-Art Director-
3----ATG Dynamo Platform-Project Manager-
4----Content Design and Development-Data Architect-
5----Software Engineering Experience-Design Lead-
6----Application Architecture Design-Interactive Lead-
7----Screen Design-Application Architect-
8----SQL/Oracle Database Programming-Designer-
9----Interwoven Platform-Business Analyst 1-
10----Interface Design/Development-Software Engineer 1-