Network Level Measures
Measure Value Row count 44.000 Column count 44.000 Link count 621.000 Density 0.328 Components of 1 node (isolates) 0 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.405 Characteristic path length 2.512 Clustering coefficient 0.584 Network levels (diameter) 6.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.558 Krackhardt hierarchy 0.089 Krackhardt upperboundedness 1.000 Degree centralization 0.312 Betweenness centralization 0.090 Closeness centralization 0.611 Eigenvector centralization 0.309 Reciprocal (symmetric)? No (40% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.021 0.410 0.112 0.100 Total degree centrality [Unscaled] 16.000 317.000 86.636 77.729 In-degree centrality 0.016 0.408 0.112 0.110 In-degree centrality [Unscaled] 6.000 158.000 43.318 42.564 Out-degree centrality 0.000 0.413 0.112 0.109 Out-degree centrality [Unscaled] 0.000 160.000 43.318 42.273 Eigenvector centrality 0.033 0.467 0.173 0.125 Eigenvector centrality [Unscaled] 0.024 0.330 0.122 0.089 Eigenvector centrality per component 0.024 0.330 0.122 0.089 Closeness centrality 0.003 0.694 0.399 0.123 Closeness centrality [Unscaled] 0.000 0.016 0.009 0.003 In-Closeness centrality 0.047 0.085 0.050 0.008 In-Closeness centrality [Unscaled] 0.001 0.002 0.001 0.000 Betweenness centrality 0.000 0.116 0.028 0.030 Betweenness centrality [Unscaled] 0.000 208.853 49.845 53.786 Hub centrality 0.000 0.481 0.161 0.140 Authority centrality 0.019 0.518 0.159 0.142 Information centrality 0.000 0.034 0.023 0.009 Information centrality [Unscaled] 0.000 12.184 8.135 3.389 Clique membership count 2.000 122.000 25.773 31.916 Simmelian ties 0.000 0.744 0.183 0.186 Simmelian ties [Unscaled] 0.000 32.000 7.864 8.016 Clustering coefficient 0.302 0.847 0.584 0.150
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: BKHAMC (size: 44, density: 0.328224)
Rank Agent Value Unscaled Context* 1 A2 0.410 317.000 1.149 2 A7 0.375 290.000 0.656 3 A18 0.333 258.000 0.072 4 A33 0.296 229.000 -0.457 5 A16 0.270 209.000 -0.822 6 A31 0.262 203.000 -0.932 7 A14 0.227 176.000 -1.424 8 A39 0.218 169.000 -1.552 9 A44 0.167 129.000 -2.282 10 A4 0.160 124.000 -2.373 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.112 Mean in random network: 0.328 Std.dev: 0.100 Std.dev in random network: 0.071 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): BKHAMC
Rank Agent Value Unscaled 1 A31 0.408 158.000 2 A2 0.406 157.000 3 A7 0.401 155.000 4 A18 0.401 155.000 5 A33 0.333 129.000 6 A16 0.214 83.000 7 A4 0.204 79.000 8 A40 0.176 68.000 9 A44 0.155 60.000 10 A43 0.145 56.000 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): BKHAMC
Rank Agent Value Unscaled 1 A2 0.413 160.000 2 A7 0.349 135.000 3 A14 0.326 126.000 4 A16 0.326 126.000 5 A39 0.295 114.000 6 A18 0.266 103.000 7 A33 0.258 100.000 8 A1 0.238 92.000 9 A10 0.238 92.000 10 A8 0.194 75.000 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: BKHAMC (size: 44, density: 0.328224)
Rank Agent Value Unscaled Context* 1 A7 0.467 0.330 -0.648 2 A2 0.440 0.311 -0.737 3 A18 0.406 0.287 -0.848 4 A16 0.394 0.279 -0.889 5 A31 0.388 0.274 -0.909 6 A33 0.370 0.261 -0.968 7 A39 0.339 0.239 -1.070 8 A14 0.315 0.223 -1.148 9 A44 0.294 0.208 -1.215 10 A10 0.281 0.199 -1.258 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.173 Mean in random network: 0.665 Std.dev: 0.125 Std.dev in random network: 0.305 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): BKHAMC
Rank Agent Value 1 A7 0.330 2 A2 0.311 3 A18 0.287 4 A16 0.279 5 A31 0.274 6 A33 0.261 7 A39 0.239 8 A14 0.223 9 A44 0.208 10 A10 0.199 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: BKHAMC (size: 44, density: 0.328224)
Rank Agent Value Unscaled Context* 1 A18 0.694 0.016 3.112 2 A7 0.581 0.014 -0.602 3 A24 0.544 0.013 -1.816 4 A14 0.538 0.013 -2.041 5 A33 0.524 0.012 -2.473 6 A20 0.512 0.012 -2.886 7 A39 0.512 0.012 -2.886 8 A17 0.494 0.011 -3.469 9 A21 0.489 0.011 -3.654 10 A31 0.489 0.011 -3.654 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.399 Mean in random network: 0.599 Std.dev: 0.123 Std.dev in random network: 0.030 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): BKHAMC
Rank Agent Value Unscaled 1 A41 0.085 0.002 2 A23 0.084 0.002 3 A16 0.049 0.001 4 A24 0.049 0.001 5 A9 0.049 0.001 6 A19 0.049 0.001 7 A28 0.049 0.001 8 A26 0.049 0.001 9 A21 0.049 0.001 10 A27 0.049 0.001 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: BKHAMC (size: 44, density: 0.328224)
Rank Agent Value Unscaled Context* 1 A24 0.116 208.853 8.797 2 A31 0.107 192.603 7.961 3 A7 0.102 183.949 7.516 4 A18 0.099 179.033 7.263 5 A33 0.062 111.709 3.801 6 A2 0.055 99.514 3.174 7 A43 0.052 93.632 2.871 8 A16 0.050 89.899 2.679 9 A14 0.049 89.001 2.633 10 A28 0.049 88.618 2.613 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.028 Mean in random network: 0.021 Std.dev: 0.030 Std.dev in random network: 0.011 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): BKHAMC
Rank Agent Value 1 A2 0.481 2 A16 0.465 3 A7 0.462 4 A39 0.409 5 A14 0.406 6 A10 0.365 7 A18 0.349 8 A33 0.341 9 A44 0.252 10 A8 0.241 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): BKHAMC
Rank Agent Value 1 A31 0.518 2 A7 0.509 3 A18 0.509 4 A2 0.489 5 A33 0.449 6 A4 0.337 7 A16 0.304 8 A40 0.268 9 A44 0.265 10 A39 0.228 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): BKHAMC
Rank Agent Value Unscaled 1 A2 0.034 12.184 2 A14 0.034 12.029 3 A7 0.034 12.019 4 A16 0.034 12.003 5 A39 0.033 11.887 6 A1 0.033 11.668 7 A18 0.033 11.663 8 A33 0.033 11.655 9 A10 0.033 11.654 10 A8 0.031 11.269 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): BKHAMC
Rank Agent Value 1 A18 122.000 2 A2 109.000 3 A14 100.000 4 A7 95.000 5 A33 88.000 6 A31 81.000 7 A16 51.000 8 A43 51.000 9 A39 42.000 10 A1 41.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): BKHAMC
Rank Agent Value Unscaled 1 A18 0.744 32.000 2 A7 0.628 27.000 3 A2 0.605 26.000 4 A33 0.605 26.000 5 A16 0.465 20.000 6 A31 0.442 19.000 7 A14 0.372 16.000 8 A39 0.349 15.000 9 A44 0.256 11.000 10 A4 0.233 10.000 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): BKHAMC
Rank Agent Value 1 A42 0.847 2 A15 0.813 3 A34 0.791 4 A26 0.790 5 A30 0.786 6 A3 0.773 7 A41 0.767 8 A23 0.744 9 A35 0.727 10 A9 0.721
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 A24 A18 A7 A7 A31 A41 A2 A2 2 A31 A7 A2 A2 A2 A23 A7 A7 3 A7 A24 A18 A18 A7 A16 A14 A18 4 A18 A14 A16 A16 A18 A24 A16 A33 5 A33 A33 A31 A31 A33 A9 A39 A16 6 A2 A20 A33 A33 A16 A19 A18 A31 7 A43 A39 A39 A39 A4 A28 A33 A14 8 A16 A17 A14 A14 A40 A26 A1 A39 9 A14 A21 A44 A44 A44 A21 A10 A44 10 A28 A31 A10 A10 A43 A27 A8 A4