Network Level Measures
Measure Value Row count 16.000 Column count 24.000 Link count 72.000 Density 0.188
Node Level Measures
Measure Min Max Avg Stddev In-degree centrality 0.063 0.438 0.188 0.087 In-degree centrality [Unscaled] 1.000 7.000 3.000 1.384 Out-degree centrality 0.042 0.292 0.188 0.068 Out-degree centrality [Unscaled] 1.000 7.000 4.500 1.620
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
Rank Task Value Unscaled 1 Reporting 0.438 7.000 2 Development ? Screens 0.313 5.000 3 Development ? Content 0.313 5.000 4 Application Architecture ? Flows 0.250 4.000 5 Application Architecture ? Content Management 0.250 4.000 6 Testing ? Integration 0.250 4.000 7 Testing ? System 0.250 4.000 8 Testing ? User Acceptance 0.250 4.000 9 Administration 0.188 3.000 10 Detailed Supervision 0.188 3.000 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 task
Rank Agent Value Unscaled 1 Technical Lead 0.292 7.000 2 Design Lead 0.292 7.000 3 Art Director 0.250 6.000 4 Application Architect 0.250 6.000 5 Designer 0.250 6.000 6 Software Engineer 2 0.250 6.000 7 Project Manager 0.167 4.000 8 Interactive Lead 0.167 4.000 9 Data Architect 0.167 4.000 10 Web Developer 0.167 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 - - - - Reporting - Technical Lead - 2 - - - - Development ? Screens - Design Lead - 3 - - - - Development ? Content - Art Director - 4 - - - - Application Architecture ? Flows - Application Architect - 5 - - - - Application Architecture ? Content Management - Designer - 6 - - - - Testing ? Integration - Software Engineer 2 - 7 - - - - Testing ? System - Project Manager - 8 - - - - Testing ? User Acceptance - Interactive Lead - 9 - - - - Administration - Data Architect - 10 - - - - Detailed Supervision - Web Developer -