Block Model - Newman's Clustering Algorithm
Network Level Measures
Measure Value Row count 684.000 Column count 684.000 Link count 1300.000 Density 0.003 Components of 1 node (isolates) 166 Components of 2 nodes (dyadic isolates) 5 Components of 3 or more nodes 2 Reciprocity 0.063 Characteristic path length 3.591 Clustering coefficient 0.194 Network levels (diameter) 8.000 Network fragmentation 0.462 Krackhardt connectedness 0.538 Krackhardt efficiency 0.994 Krackhardt hierarchy 0.825 Krackhardt upperboundedness 0.634 Degree centralization 0.063 Betweenness centralization 0.040 Closeness centralization 0.000 Eigenvector centralization 0.373 Reciprocal (symmetric)? No (6% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.065 0.003 0.007 Total degree centrality [Unscaled] 0.000 89.000 3.773 9.132 In-degree centrality 0.000 0.085 0.003 0.009 In-degree centrality [Unscaled] 0.000 58.000 1.901 5.933 Out-degree centrality 0.000 0.056 0.003 0.006 Out-degree centrality [Unscaled] 0.000 38.000 1.901 3.880 Eigenvector centrality 0.000 0.396 0.024 0.048 Eigenvector centrality [Unscaled] 0.000 0.280 0.017 0.034 Eigenvector centrality per component 0.000 0.206 0.013 0.025 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 0.004 0.002 0.001 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.041 0.001 0.003 Betweenness centrality [Unscaled] 0.000 19075.123 347.825 1552.020 Hub centrality 0.000 0.426 0.024 0.048 Authority centrality 0.000 0.506 0.016 0.052 Information centrality 0.000 0.004 0.001 0.001 Information centrality [Unscaled] 0.000 1.067 0.442 0.328 Clique membership count 0.000 143.000 2.344 10.514 Simmelian ties 0.000 0.012 0.000 0.001 Simmelian ties [Unscaled] 0.000 8.000 0.079 0.626 Clustering coefficient 0.000 1.000 0.194 0.326
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: Son (size: 684, density: 0.00277863)
Rank Agent Value Unscaled Context* 1 John 0.065 89.000 30.967 2 William 0.059 80.000 27.696 3 Corteen 0.053 73.000 25.152 4 Cannell 0.050 69.000 23.698 5 Catherine 0.044 60.000 20.427 6 Thomas 0.043 59.000 20.063 7 Clague 0.040 54.000 18.246 8 Jane 0.037 51.000 17.156 9 Margaret 0.037 50.000 16.792 10 Mylrea 0.037 50.000 16.792 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.003 Mean in random network: 0.003 Std.dev: 0.007 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): Son
Rank Agent Value Unscaled 1 Corteen 0.085 58.000 2 Cannell 0.083 57.000 3 John 0.082 56.000 4 William 0.063 43.000 5 Catherine 0.063 43.000 6 Mylrea 0.057 39.000 7 Clague 0.056 38.000 8 Margaret 0.047 32.000 9 Christian 0.038 26.000 10 Thomas 0.037 25.000 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): Son
Rank Agent Value Unscaled 1 William 0.056 38.000 2 Thomas 0.051 35.000 3 John 0.048 33.000 4 Allen 0.044 30.000 5 Jane 0.041 28.000 6 Ann 0.031 21.000 7 James 0.029 20.000 8 Margaret 0.026 18.000 9 Elizabeth 0.025 17.000 10 Robert 0.025 17.000 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: Son (size: 684, density: 0.00277863)
Rank Agent Value Unscaled Context* 1 John 0.396 0.280 0.200 2 William 0.360 0.255 -0.028 3 Corteen 0.351 0.248 -0.085 4 Catherine 0.315 0.223 -0.312 5 Cannell 0.306 0.217 -0.365 6 Thomas 0.295 0.209 -0.436 7 Jane 0.286 0.202 -0.495 8 Mylrea 0.271 0.192 -0.587 9 Ann 0.253 0.179 -0.701 10 Margaret 0.248 0.176 -0.730 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.024 Mean in random network: 0.364 Std.dev: 0.048 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): Son
Rank Agent Value 1 John 0.206 2 William 0.187 3 Corteen 0.182 4 Catherine 0.163 5 Cannell 0.159 6 Thomas 0.153 7 Jane 0.148 8 Mylrea 0.141 9 Ann 0.131 10 Margaret 0.129 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: Son (size: 684, density: 0.00277863)
Rank Agent Value Unscaled Context* 1 Core 0.002 0.000 -4.674 2 Caine 0.002 0.000 -4.675 3 Harrison 0.002 0.000 -4.675 4 Nye 0.002 0.000 -4.675 5 Taggart 0.002 0.000 -4.675 6 Bills 0.002 0.000 -4.675 7 Elliot 0.002 0.000 -4.675 8 Adie 0.002 0.000 -4.675 9 Burgess 0.002 0.000 -4.675 10 Fitzsimmons 0.002 0.000 -4.675 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.002 Mean in random network: 0.077 Std.dev: 0.000 Std.dev in random network: 0.016 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): Son
Rank Agent Value Unscaled 1 Hazel 0.004 0.000 2 Gavin 0.004 0.000 3 Elaine 0.004 0.000 4 PeterCaine 0.004 0.000 5 Pamela 0.004 0.000 6 Marvin 0.004 0.000 7 Ross 0.004 0.000 8 Sidebotham 0.004 0.000 9 Clague-Kline 0.004 0.000 10 Gabrael 0.004 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: Son (size: 684, density: 0.00277863)
Rank Agent Value Unscaled Context* 1 John 0.041 19075.123 0.130 2 William 0.036 16714.063 0.112 3 Thomas 0.024 11079.014 0.067 4 Catherine 0.024 10980.281 0.066 5 Clague 0.023 10514.321 0.063 6 Allen 0.022 10329.829 0.061 7 Jane 0.022 10223.938 0.060 8 Cannell 0.019 8960.884 0.050 9 Corteen 0.019 8841.338 0.050 10 Margaret 0.014 6673.203 0.032 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.001 Mean in random network: 0.005 Std.dev: 0.003 Std.dev in random network: 0.273 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): Son
Rank Agent Value 1 William 0.426 2 John 0.410 3 Jane 0.346 4 Thomas 0.342 5 Ann 0.303 6 Allen 0.265 7 Catherine 0.255 8 Kelly 0.242 9 James 0.241 10 Mylrea 0.237 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): Son
Rank Agent Value 1 Corteen 0.506 2 Catherine 0.437 3 Cannell 0.421 4 John 0.401 5 Mylrea 0.384 6 Clague 0.317 7 William 0.316 8 Margaret 0.298 9 Anne 0.290 10 Thomas 0.288 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Son
Rank Agent Value Unscaled 1 William 0.004 1.067 2 Thomas 0.004 1.064 3 Allen 0.004 1.061 4 Jane 0.003 1.054 5 John 0.003 1.053 6 Ann 0.003 1.044 7 James 0.003 1.041 8 Caine 0.003 1.033 9 Robert 0.003 1.032 10 Elizabeth 0.003 1.032 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): Son
Rank Agent Value 1 John 143.000 2 William 91.000 3 Corteen 89.000 4 Catherine 81.000 5 Jane 69.000 6 Thomas 64.000 7 Cannell 59.000 8 Mylrea 59.000 9 Allen 53.000 10 Margaret 48.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Son
Rank Agent Value Unscaled 1 John 0.012 8.000 2 Catherine 0.012 8.000 3 Thomas 0.009 6.000 4 Mylrea 0.009 6.000 5 Cannell 0.006 4.000 6 Allen 0.004 3.000 7 Margaret 0.004 3.000 8 William 0.004 3.000 9 Corteen 0.004 3.000 10 Jane 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): Son
Rank Agent Value 1 Newsome 1.000 2 (Jemmy 1.000 3 J. 1.000 4 Clarence 1.000 5 Eddison 1.000 6 Calvin 1.000 7 Borga 1.000 8 Edison 1.000 9 III 1.000 10 Leslie 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 Core John John Corteen Hazel William John 2 William Caine William William Cannell Gavin Thomas William 3 Thomas Harrison Corteen Corteen John Elaine John Corteen 4 Catherine Nye Catherine Catherine William PeterCaine Allen Cannell 5 Clague Taggart Cannell Cannell Catherine Pamela Jane Catherine 6 Allen Bills Thomas Thomas Mylrea Marvin Ann Thomas 7 Jane Elliot Jane Jane Clague Ross James Clague 8 Cannell Adie Mylrea Mylrea Margaret Sidebotham Margaret Jane 9 Corteen Burgess Ann Ann Christian Clague-Kline Elizabeth Margaret 10 Margaret Fitzsimmons Margaret Margaret Thomas Gabrael Robert Mylrea