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Network Level Measures

Measure	Value
Row count	6.000
Column count	152.000
Link count	79.000
Density	0.087

Node Level Measures

Measure	Min	Max	Avg	Stddev
In-degree centrality	0.000	0.333	0.087	0.085
In-degree centrality [Unscaled]	0.000	2.000	0.520	0.513
Out-degree centrality	0.000	0.171	0.087	0.062
Out-degree centrality [Unscaled]	0.000	26.000	13.167	9.388

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	T9	0.333	2.000
2	T1	0.167	1.000
3	T2	0.167	1.000
4	T3	0.167	1.000
5	T4	0.167	1.000
6	T5	0.167	1.000
7	T6	0.167	1.000
8	T7	0.167	1.000
9	T8	0.167	1.000
10	T11	0.167	1.000

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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): agent x task

Rank	Agent	Value	Unscaled
1	A4	0.171	26.000
2	A5	0.145	22.000
3	A1	0.118	18.000
4	A2	0.046	7.000
5	A6	0.039	6.000

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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	-	-	-	-	T9	-	A4	-
2	-	-	-	-	T1	-	A5	-
3	-	-	-	-	T2	-	A1	-
4	-	-	-	-	T3	-	A2	-
5	-	-	-	-	T4	-	A6	-
6	-	-	-	-	T5	-	A3	-
7	-	-	-	-	T6	-	-	-
8	-	-	-	-	T7	-	-	-
9	-	-	-	-	T8	-	-	-
10	-	-	-	-	T11	-	-	-