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Network Level Measures

Measure	Value
Row count	16.000
Column count	16.000
Link count	29.000
Density	0.242
Components of 1 node (isolates)	0
Components of 2 nodes (dyadic isolates)	0
Components of 3 or more nodes	2
Reciprocity	1.000
Characteristic path length	1.917
Clustering coefficient	0.684
Network levels (diameter)	4.000
Network fragmentation	0.400
Krackhardt connectedness	0.600
Krackhardt efficiency	0.741
Krackhardt hierarchy	0.000
Krackhardt upperboundedness	1.000
Degree centralization	0.257
Betweenness centralization	0.273
Closeness centralization	0.087
Eigenvector centralization	0.446
Reciprocal (symmetric)?	Yes

Node Level Measures

Measure	Min	Max	Avg	Stddev
Total degree centrality	0.133	0.467	0.242	0.091
Total degree centrality [Unscaled]	2.000	7.000	3.625	1.364
In-degree centrality	0.133	0.467	0.242	0.091
In-degree centrality [Unscaled]	2.000	7.000	3.625	1.364
Out-degree centrality	0.133	0.467	0.242	0.091
Out-degree centrality [Unscaled]	2.000	7.000	3.625	1.364
Eigenvector centrality	0.000	0.648	0.258	0.241
Eigenvector centrality [Unscaled]	0.000	0.458	0.183	0.171
Eigenvector centrality per component	0.031	0.344	0.168	0.104
Closeness centrality	0.077	0.190	0.150	0.043
Closeness centrality [Unscaled]	0.005	0.013	0.010	0.003
In-Closeness centrality	0.077	0.190	0.150	0.043
In-Closeness centrality [Unscaled]	0.005	0.013	0.010	0.003
Betweenness centrality	0.000	0.295	0.039	0.077
Betweenness centrality [Unscaled]	0.000	31.000	4.125	8.054
Hub centrality	0.000	0.648	0.258	0.241
Authority centrality	0.000	0.648	0.258	0.241
Information centrality	0.025	0.075	0.063	0.022
Information centrality [Unscaled]	-0.000	-0.000	-0.000	0.000
Clique membership count	0.000	3.000	1.188	0.726
Simmelian ties	0.000	0.400	0.200	0.108
Simmelian ties [Unscaled]	0.000	6.000	3.000	1.620
Clustering coefficient	0.000	1.000	0.684	0.374

Key Nodes

This chart shows the Agent that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Agent was ranked in the top three.

Total degree centrality

The Total Degree Centrality of a node is the normalized sum of its row and column degrees. Individuals or organizations who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are "in the know" are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.

Input network: GAMAPOS (size: 16, density: 0.241667)

Rank	Agent	Value	Unscaled	Context*
1	MASIL	0.467	7.000	2.102
2	UKUDZ	0.400	6.000	1.479
3	GAHUK	0.333	5.000	0.857
4	OVE	0.267	4.000	0.234
5	GEHAM	0.267	4.000	0.234
6	ASARO	0.267	4.000	0.234
7	UHETO	0.267	4.000	0.234
8	GAVEV	0.200	3.000	-0.389
9	KOTUN	0.200	3.000	-0.389
10	NAGAM	0.200	3.000	-0.389

* Number of standard deviations from the mean of a random network of the same size and density

Mean:	0.242	Mean in random network:	0.242
Std.dev:	0.091	Std.dev in random network:	0.107

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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): GAMAPOS

Rank	Agent	Value	Unscaled
1	MASIL	0.467	7.000
2	UKUDZ	0.400	6.000
3	GAHUK	0.333	5.000
4	OVE	0.267	4.000
5	GEHAM	0.267	4.000
6	ASARO	0.267	4.000
7	UHETO	0.267	4.000
8	GAVEV	0.200	3.000
9	KOTUN	0.200	3.000
10	NAGAM	0.200	3.000

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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): GAMAPOS

Rank	Agent	Value	Unscaled
1	MASIL	0.467	7.000
2	UKUDZ	0.400	6.000
3	GAHUK	0.333	5.000
4	OVE	0.267	4.000
5	GEHAM	0.267	4.000
6	ASARO	0.267	4.000
7	UHETO	0.267	4.000
8	GAVEV	0.200	3.000
9	KOTUN	0.200	3.000
10	NAGAM	0.200	3.000

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Eigenvector centrality

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Leaders of strong cliques are individuals who or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent matrices.

Input network: GAMAPOS (size: 16, density: 0.241667)

Rank	Agent	Value	Unscaled	Context*
1	MASIL	0.648	0.458	0.492
2	UKUDZ	0.625	0.442	0.415
3	GAHUK	0.585	0.413	0.281
4	GEHAM	0.506	0.358	0.020
5	ASARO	0.506	0.358	0.020
6	OVE	0.447	0.316	-0.177
7	ALIKA	0.229	0.162	-0.897
8	UHETO	0.187	0.132	-1.037
9	NAGAM	0.175	0.123	-1.079
10	NOTOH	0.090	0.064	-1.359

* Number of standard deviations from the mean of a random network of the same size and density

Mean:	0.258	Mean in random network:	0.500
Std.dev:	0.241	Std.dev in random network:	0.302

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Eigenvector centrality per component

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Each component is extracted as a separate network, Eigenvector Centrality is computed on it and scaled according to the component size. The scores are then combined into a single result vector.

Input network(s): GAMAPOS

Rank	Agent	Value
1	MASIL	0.344
2	UKUDZ	0.331
3	GAHUK	0.310
4	GEHAM	0.268
5	ASARO	0.268
6	OVE	0.237
7	GAVEV	0.125
8	KOTUN	0.125
9	NAGAD	0.125
10	GAMA	0.125

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Closeness centrality

The average closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the average distance in the network from the node to all other nodes.

Input network: GAMAPOS (size: 16, density: 0.241667)

Rank	Agent	Value	Unscaled	Context*
1	MASIL	0.190	0.013	-4.608
2	UKUDZ	0.181	0.012	-4.748
3	UHETO	0.181	0.012	-4.748
4	NAGAM	0.179	0.012	-4.780
5	GAHUK	0.179	0.012	-4.780
6	OVE	0.176	0.012	-4.812
7	GEHAM	0.176	0.012	-4.812
8	ASARO	0.176	0.012	-4.812
9	NOTOH	0.167	0.011	-4.962
10	KOHIK	0.165	0.011	-4.989

* Number of standard deviations from the mean of a random network of the same size and density

Mean:	0.150	Mean in random network:	0.493
Std.dev:	0.043	Std.dev in random network:	0.066

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In-Closeness centrality

The average closeness of a node from the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network to the node and from all other nodes.

Input network(s): GAMAPOS

Rank	Agent	Value	Unscaled
1	MASIL	0.190	0.013
2	UKUDZ	0.181	0.012
3	UHETO	0.181	0.012
4	NAGAM	0.179	0.012
5	GAHUK	0.179	0.012
6	OVE	0.176	0.012
7	GEHAM	0.176	0.012
8	ASARO	0.176	0.012
9	NOTOH	0.167	0.011
10	KOHIK	0.165	0.011

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Betweenness centrality

The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.

Input network: GAMAPOS (size: 16, density: 0.241667)

Rank	Agent	Value	Unscaled	Context*
1	MASIL	0.295	31.000	4.156
2	UHETO	0.148	15.500	1.262
3	NAGAM	0.071	7.500	-0.231
4	UKUDZ	0.059	6.167	-0.480
5	OVE	0.033	3.500	-0.978
6	NOTOH	0.013	1.333	-1.383
7	GAHUK	0.006	0.667	-1.507
8	SEUVE	0.003	0.333	-1.569

* Number of standard deviations from the mean of a random network of the same size and density

Mean:	0.039	Mean in random network:	0.083
Std.dev:	0.077	Std.dev in random network:	0.051

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Hub centrality

A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.

Input network(s): GAMAPOS

Rank	Agent	Value
1	MASIL	0.648
2	UKUDZ	0.625
3	GAHUK	0.585
4	GEHAM	0.506
5	ASARO	0.506
6	OVE	0.447
7	ALIKA	0.229
8	UHETO	0.187
9	NAGAM	0.175
10	NOTOH	0.090

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Authority centrality

A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others. Technically, an agent is authority-central if its in-links are from agents that have are sending links to many others. The scientific name of this measure is authority centrality and it is calculated on agent by agent matrices.

Input network(s): GAMAPOS

Rank	Agent	Value
1	MASIL	0.648
2	UKUDZ	0.625
3	GAHUK	0.585
4	GEHAM	0.506
5	ASARO	0.506
6	OVE	0.447
7	ALIKA	0.229
8	UHETO	0.187
9	NAGAM	0.175
10	NOTOH	0.090

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Information centrality

Calculate the Stephenson and Zelen information centrality measure for each node.

Input network(s): GAMAPOS

Rank	Agent	Value
1	KOHIK	0.075
2	SEUVE	0.075
3	ALIKA	0.075
4	NOTOH	0.075
5	NAGAM	0.075
6	UHETO	0.075
7	OVE	0.075
8	GEHAM	0.075
9	ASARO	0.075
10	GAHUK	0.075

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Clique membership count

The number of distinct cliques to which each node belongs. Individuals or organizations who are high in number of cliques are those that belong to a large number of distinct cliques. A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. The scientific name of this measure is clique count and it is calculated on the agent by agent matrices.

Input network(s): GAMAPOS

Rank	Agent	Value
1	UKUDZ	3.000
2	OVE	2.000
3	GAHUK	2.000
4	MASIL	2.000
5	GAVEV	1.000
6	KOTUN	1.000
7	ALIKA	1.000
8	NOTOH	1.000
9	KOHIK	1.000
10	GEHAM	1.000

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Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): GAMAPOS

Rank	Agent	Value	Unscaled
1	UKUDZ	0.400	6.000
2	GAHUK	0.333	5.000
3	MASIL	0.333	5.000
4	OVE	0.267	4.000
5	GEHAM	0.267	4.000
6	ASARO	0.267	4.000
7	GAVEV	0.200	3.000
8	KOTUN	0.200	3.000
9	NAGAD	0.200	3.000
10	GAMA	0.200	3.000

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Clustering coefficient

Measures the degree of clustering in a network by averaging the clustering coefficient of each node, which is defined as the density of the node's ego network.

Input network(s): GAMAPOS

Rank	Agent	Value
1	GAVEV	1.000
2	KOTUN	1.000
3	ALIKA	1.000
4	KOHIK	1.000
5	GEHAM	1.000
6	ASARO	1.000
7	NAGAD	1.000
8	GAMA	1.000
9	GAHUK	0.800
10	OVE	0.667

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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	MASIL	MASIL	MASIL	MASIL	MASIL	MASIL	MASIL	MASIL
2	UHETO	UKUDZ	UKUDZ	UKUDZ	UKUDZ	UKUDZ	UKUDZ	UKUDZ
3	NAGAM	UHETO	GAHUK	GAHUK	GAHUK	UHETO	GAHUK	GAHUK
4	UKUDZ	NAGAM	GEHAM	GEHAM	OVE	NAGAM	OVE	OVE
5	OVE	GAHUK	ASARO	ASARO	GEHAM	GAHUK	GEHAM	GEHAM
6	NOTOH	OVE	OVE	OVE	ASARO	OVE	ASARO	ASARO
7	GAHUK	GEHAM	ALIKA	GAVEV	UHETO	GEHAM	UHETO	UHETO
8	SEUVE	ASARO	UHETO	KOTUN	GAVEV	ASARO	GAVEV	GAVEV
9	GAVEV	NOTOH	NAGAM	NAGAD	KOTUN	NOTOH	KOTUN	KOTUN
10	KOTUN	KOHIK	NOTOH	GAMA	NAGAM	KOHIK	NAGAM	NAGAM