Block Model - Newman's Clustering Algorithm
Network Level Measures
Measure Value Row count 684.000 Column count 684.000 Link count 1140.000 Density 0.002 Components of 1 node (isolates) 211 Components of 2 nodes (dyadic isolates) 7 Components of 3 or more nodes 3 Reciprocity 0.046 Characteristic path length 3.574 Clustering coefficient 0.224 Network levels (diameter) 9.000 Network fragmentation 0.562 Krackhardt connectedness 0.438 Krackhardt efficiency 0.994 Krackhardt hierarchy 0.868 Krackhardt upperboundedness 0.635 Degree centralization 0.059 Betweenness centralization 0.034 Closeness centralization 0.001 Eigenvector centralization 0.425 Reciprocal (symmetric)? No (4% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.061 0.002 0.006 Total degree centrality [Unscaled] 0.000 168.000 6.596 16.060 In-degree centrality 0.000 0.073 0.002 0.007 In-degree centrality [Unscaled] 0.000 100.000 3.333 10.195 Out-degree centrality 0.000 0.051 0.002 0.005 Out-degree centrality [Unscaled] 0.000 70.000 3.333 7.169 Eigenvector centrality 0.000 0.446 0.023 0.049 Eigenvector centrality [Unscaled] 0.000 0.316 0.016 0.035 Eigenvector centrality per component 0.000 0.209 0.011 0.023 Closeness centrality 0.001 0.002 0.002 0.000 Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 In-Closeness centrality 0.001 0.003 0.002 0.001 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.034 0.001 0.003 Betweenness centrality [Unscaled] 0.000 16021.296 261.656 1215.794 Hub centrality 0.000 0.491 0.022 0.049 Authority centrality 0.000 0.461 0.016 0.052 Information centrality 0.000 0.005 0.001 0.001 Information centrality [Unscaled] 0.000 4.417 1.181 1.174 Clique membership count 0.000 121.000 1.806 8.346 Simmelian ties 0.000 0.007 0.000 0.001 Simmelian ties [Unscaled] 0.000 5.000 0.038 0.357 Clustering coefficient 0.000 1.000 0.224 0.361
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: Daughter (size: 684, density: 0.00243665)
Rank Agent Value Unscaled Context* 1 John 0.061 168.000 31.304 2 William 0.045 122.000 22.379 3 Margaret 0.042 116.000 21.215 4 Catherine 0.042 114.000 20.827 5 Cannell 0.042 114.000 20.827 6 Thomas 0.038 104.000 18.886 7 Mylrea 0.037 100.000 18.110 8 Corteen 0.034 94.000 16.946 9 Clague 0.031 84.000 15.006 10 James 0.030 82.000 14.618 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.002 Mean in random network: 0.002 Std.dev: 0.006 Std.dev in random network: 0.002 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): Daughter
Rank Agent Value Unscaled 1 John 0.073 100.000 2 Cannell 0.063 86.000 3 Mylrea 0.061 84.000 4 Catherine 0.058 80.000 5 William 0.051 70.000 6 Corteen 0.051 70.000 7 Margaret 0.048 66.000 8 Clague 0.044 60.000 9 Christian 0.039 54.000 10 James 0.038 52.000 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): Daughter
Rank Agent Value Unscaled 1 John 0.051 70.000 2 Allen 0.045 62.000 3 Thomas 0.039 54.000 4 William 0.039 54.000 5 Margaret 0.038 52.000 6 Mary 0.031 42.000 7 Jane 0.026 36.000 8 Catherine 0.026 36.000 9 Ann 0.025 34.000 10 James 0.023 32.000 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: Daughter (size: 684, density: 0.00243665)
Rank Agent Value Unscaled Context* 1 John 0.446 0.316 0.548 2 William 0.339 0.240 -0.126 3 Margaret 0.317 0.224 -0.263 4 Catherine 0.313 0.222 -0.286 5 Cannell 0.312 0.221 -0.293 6 Thomas 0.306 0.217 -0.331 7 Allen 0.286 0.202 -0.458 8 Mylrea 0.272 0.192 -0.547 9 Corteen 0.264 0.187 -0.595 10 Jane 0.257 0.181 -0.643 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.023 Mean in random network: 0.359 Std.dev: 0.049 Std.dev in random network: 0.159 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): Daughter
Rank Agent Value 1 John 0.209 2 William 0.159 3 Margaret 0.149 4 Catherine 0.147 5 Cannell 0.146 6 Thomas 0.143 7 Allen 0.134 8 Mylrea 0.127 9 Corteen 0.124 10 Jane 0.120 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: Daughter (size: 684, density: 0.00243665)
Rank Agent Value Unscaled Context* 1 Ball 0.002 0.000 -4.664 2 Taggart 0.002 0.000 -4.664 3 Fay 0.002 0.000 -4.664 4 Smallwood 0.002 0.000 -4.665 5 Martha 0.002 0.000 -4.665 6 Harrison 0.002 0.000 -4.665 7 Downham 0.002 0.000 -4.665 8 Timothy 0.002 0.000 -4.665 9 Richard 0.002 0.000 -4.665 10 Adie 0.002 0.000 -4.665 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.002 Mean in random network: 0.071 Std.dev: 0.000 Std.dev in random network: 0.015 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): Daughter
Rank Agent Value Unscaled 1 Josephine 0.003 0.000 2 Yvonne 0.003 0.000 3 Gracia 0.003 0.000 4 SteichStytch 0.003 0.000 5 Clague-Kline 0.003 0.000 6 EmmaHester 0.003 0.000 7 JaneCaine 0.003 0.000 8 Dale 0.003 0.000 9 Randall 0.003 0.000 10 Jeffrey 0.003 0.000 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: Daughter (size: 684, density: 0.00243665)
Rank Agent Value Unscaled Context* 1 John 0.034 16021.296 0.101 2 William 0.024 10979.255 0.063 3 Margaret 0.023 10895.911 0.063 4 Cannell 0.017 8116.170 0.042 5 Jane 0.017 7870.289 0.040 6 Thomas 0.016 7629.966 0.038 7 Catherine 0.015 6870.325 0.032 8 Clague 0.014 6310.110 0.028 9 Ann 0.013 6002.424 0.026 10 Corteen 0.013 5961.362 0.025 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.001 Mean in random network: 0.006 Std.dev: 0.003 Std.dev in random network: 0.286 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): Daughter
Rank Agent Value 1 John 0.491 2 Allen 0.390 3 Thomas 0.375 4 William 0.353 5 Margaret 0.341 6 Jane 0.268 7 Robert 0.252 8 Catherine 0.249 9 Elizabeth 0.243 10 James 0.236 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): Daughter
Rank Agent Value 1 Cannell 0.461 2 John 0.449 3 Catherine 0.382 4 Margaret 0.371 5 Corteen 0.369 6 Mylrea 0.355 7 William 0.339 8 Clague 0.314 9 Christian 0.296 10 Thomas 0.265 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Daughter
Rank Agent Value Unscaled 1 John 0.005 4.417 2 Allen 0.005 4.390 3 William 0.005 4.345 4 Thomas 0.005 4.335 5 Margaret 0.005 4.303 6 Mary 0.005 4.247 7 Jane 0.005 4.176 8 Catherine 0.005 4.156 9 Ann 0.005 4.155 10 James 0.005 4.120 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): Daughter
Rank Agent Value 1 John 121.000 2 Catherine 71.000 3 Thomas 66.000 4 William 62.000 5 Allen 51.000 6 Margaret 51.000 7 Cannell 50.000 8 Corteen 46.000 9 Ann 41.000 10 Jane 40.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Daughter
Rank Agent Value Unscaled 1 Thomas 0.007 5.000 2 Cannell 0.007 5.000 3 Margaret 0.004 3.000 4 Catherine 0.004 3.000 5 William 0.003 2.000 6 Robert 0.003 2.000 7 Christian 0.003 2.000 8 Mylrea 0.003 2.000 9 Curphey 0.003 2.000 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): Daughter
Rank Agent Value 1 the 1.000 2 Eileen 1.000 3 Janet 1.000 4 King 1.000 5 Aunt May 1.000 6 Grace 1.000 7 Roy 1.000 8 Pet 1.000 9 Cottier 1.000 10 Shelley 1.000
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 John Ball John John John Josephine John John 2 William Taggart William William Cannell Yvonne Allen William 3 Margaret Fay Margaret Margaret Mylrea Gracia Thomas Margaret 4 Cannell Smallwood Catherine Catherine Catherine SteichStytch William Catherine 5 Jane Martha Cannell Cannell William Clague-Kline Margaret Cannell 6 Thomas Harrison Thomas Thomas Corteen EmmaHester Mary Thomas 7 Catherine Downham Allen Allen Margaret JaneCaine Jane Mylrea 8 Clague Timothy Mylrea Mylrea Clague Dale Catherine Corteen 9 Ann Richard Corteen Corteen Christian Randall Ann Clague 10 Corteen Adie Jane Jane James Jeffrey James James