Calculates common social network measures on each selected input network.
Network agent x event
Network Level Measures
Measure Value Row count 18.000 Column count 14.000 Link count 89.000 Density 0.353
Node Level Measures
Measure Min Max Avg Stddev In-degree centrality 0.167 0.778 0.353 0.192 In-degree centrality [Unscaled] 3.000 14.000 6.357 3.456 Out-degree centrality 0.143 0.571 0.353 0.148 Out-degree centrality [Unscaled] 2.000 8.000 4.944 2.068
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 event
Rank Event Value Unscaled 1 E8 0.778 14.000 2 E9 0.667 12.000 3 E7 0.556 10.000 4 E5 0.444 8.000 5 E6 0.444 8.000 6 E3 0.333 6.000 7 E12 0.333 6.000 8 E10 0.278 5.000 9 E4 0.222 4.000 10 E11 0.222 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 event
Rank Agent Value Unscaled 1 EVELYN 0.571 8.000 2 THERESA 0.571 8.000 3 NORA 0.571 8.000 4 LAURA 0.500 7.000 5 BRENDA 0.500 7.000 6 SYLVIA 0.500 7.000 7 KATHERINE 0.429 6.000 8 HELEN 0.357 5.000 9 CHARLOTTE 0.286 4.000 10 FRANCES 0.286 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 - - - - E8 - EVELYN - 2 - - - - E9 - THERESA - 3 - - - - E7 - NORA - 4 - - - - E5 - LAURA - 5 - - - - E6 - BRENDA - 6 - - - - E3 - SYLVIA - 7 - - - - E12 - KATHERINE - 8 - - - - E10 - HELEN - 9 - - - - E4 - CHARLOTTE - 10 - - - - E11 - FRANCES -