Input data: agent x knowledge
Start time: Fri Oct 14 14:51:52 2011
Network Level Measures
Measure Value Row count 16.000 Column count 19.000 Link count 82.000 Density 0.270 Node Level Measures
Measure Min Max Avg Stddev In-degree centrality 0.063 0.688 0.270 0.172 In-degree centrality [Unscaled] 1.000 11.000 4.316 2.754 Out-degree centrality 0.105 0.526 0.270 0.123 Out-degree centrality [Unscaled] 2.000 10.000 5.125 2.342 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
Rank Knowledge Value Unscaled 1 Web Development (HTML) 0.688 11.000 2 Unix/Java/C++ Programming 0.563 9.000 3 ATG Dynamo Platform 0.500 8.000 4 Content Design and Development 0.438 7.000 5 Software Engineering Experience 0.375 6.000 6 Application Architecture Design 0.313 5.000 7 Screen Design 0.313 5.000 8 SQL/Oracle Database Programming 0.313 5.000 9 Interwoven Platform 0.250 4.000 10 Interface Design/Development 0.250 4.000 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
Rank Agent Value Unscaled 1 Technical Lead 0.526 10.000 2 Art Director 0.474 9.000 3 Project Manager 0.421 8.000 4 Data Architect 0.368 7.000 5 Design Lead 0.316 6.000 6 Interactive Lead 0.316 6.000 7 Application Architect 0.316 6.000 8 Designer 0.263 5.000 9 Business Analyst 1 0.211 4.000 10 Software Engineer 1 0.211 4.000 Key Nodes Table
This shows the top scoring nodes side-by-side for selected measures.
Rank Betweenness centrality Closeness centrality Eigenvector centrality Eigenvector centrality per component In-degree centrality In-Closeness centrality Out-degree centrality Total 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 -