Network Level Measures
Measure Value Row count 44.000 Column count 44.000 Link count 153.000 Density 0.162 Components of 1 node (isolates) 3 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 1.000 Characteristic path length 3.318 Clustering coefficient 0.468 Network levels (diameter) 11.000 Network fragmentation 0.133 Krackhardt connectedness 0.867 Krackhardt efficiency 0.855 Krackhardt hierarchy 0.000 Krackhardt upperboundedness 1.000 Degree centralization 0.086 Betweenness centralization 0.225 Closeness centralization 0.001 Eigenvector centralization 0.617 Reciprocal (symmetric)? Yes
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.096 0.014 0.023 Total degree centrality [Unscaled] 0.000 198.000 28.864 47.210 In-degree centrality 0.000 0.096 0.014 0.023 In-degree centrality [Unscaled] 0.000 198.000 28.864 47.210 Out-degree centrality 0.000 0.096 0.014 0.023 Out-degree centrality [Unscaled] 0.000 198.000 28.864 47.210 Eigenvector centrality 0.000 0.705 0.116 0.179 Eigenvector centrality [Unscaled] 0.000 0.499 0.082 0.126 Eigenvector centrality per component 0.000 0.465 0.077 0.118 Closeness centrality 0.000 0.007 0.006 0.002 Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 In-Closeness centrality 0.000 0.007 0.006 0.002 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.254 0.034 0.054 Betweenness centrality [Unscaled] 0.000 228.985 30.678 48.669 Hub centrality 0.000 0.705 0.116 0.179 Authority centrality 0.000 0.705 0.116 0.179 Information centrality 0.000 0.037 0.023 0.012 Information centrality [Unscaled] 0.000 3.423 2.095 1.092 Clique membership count 0.000 28.000 4.500 7.235 Simmelian ties 0.000 0.581 0.148 0.160 Simmelian ties [Unscaled] 0.000 25.000 6.364 6.869 Clustering coefficient 0.000 1.000 0.468 0.383
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: BKHAMB (size: 44, density: 0.161734)
Rank Agent Value Unscaled Context* 1 A7 0.096 198.000 -1.185 2 A2 0.079 164.000 -1.482 3 A31 0.064 133.000 -1.753 4 A33 0.059 122.000 -1.849 5 A18 0.057 117.000 -1.892 6 A43 0.047 97.000 -2.067 7 A16 0.031 65.000 -2.346 8 A22 0.019 40.000 -2.565 9 A4 0.019 39.000 -2.573 10 A24 0.015 31.000 -2.643 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.014 Mean in random network: 0.162 Std.dev: 0.023 Std.dev in random network: 0.056 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): BKHAMB
Rank Agent Value Unscaled 1 A7 0.096 198.000 2 A2 0.079 164.000 3 A31 0.064 133.000 4 A33 0.059 122.000 5 A18 0.057 117.000 6 A43 0.047 97.000 7 A16 0.031 65.000 8 A22 0.019 40.000 9 A4 0.019 39.000 10 A24 0.015 31.000 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): BKHAMB
Rank Agent Value Unscaled 1 A7 0.096 198.000 2 A2 0.079 164.000 3 A31 0.064 133.000 4 A33 0.059 122.000 5 A18 0.057 117.000 6 A43 0.047 97.000 7 A16 0.031 65.000 8 A22 0.019 40.000 9 A4 0.019 39.000 10 A24 0.015 31.000 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: BKHAMB (size: 44, density: 0.161734)
Rank Agent Value Unscaled Context* 1 A7 0.705 0.499 0.693 2 A2 0.593 0.419 0.278 3 A33 0.487 0.344 -0.112 4 A43 0.485 0.343 -0.117 5 A31 0.483 0.342 -0.124 6 A18 0.429 0.303 -0.326 7 A16 0.285 0.202 -0.854 8 A22 0.203 0.143 -1.157 9 A4 0.179 0.126 -1.247 10 A24 0.149 0.105 -1.356 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.116 Mean in random network: 0.517 Std.dev: 0.179 Std.dev in random network: 0.272 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): BKHAMB
Rank Agent Value 1 A7 0.465 2 A2 0.390 3 A33 0.321 4 A43 0.320 5 A31 0.319 6 A18 0.282 7 A16 0.188 8 A22 0.134 9 A4 0.118 10 A24 0.098 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: BKHAMB (size: 44, density: 0.161734)
Rank Agent Value Unscaled Context* 1 A31 0.007 0.000 -7.583 2 A16 0.007 0.000 -7.583 3 A42 0.007 0.000 -7.583 4 A14 0.007 0.000 -7.584 5 A39 0.007 0.000 -7.584 6 A2 0.007 0.000 -7.584 7 A43 0.007 0.000 -7.584 8 A37 0.007 0.000 -7.584 9 A10 0.007 0.000 -7.584 10 A18 0.007 0.000 -7.584 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.006 Mean in random network: 0.427 Std.dev: 0.002 Std.dev in random network: 0.055 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): BKHAMB
Rank Agent Value Unscaled 1 A31 0.007 0.000 2 A16 0.007 0.000 3 A42 0.007 0.000 4 A14 0.007 0.000 5 A39 0.007 0.000 6 A2 0.007 0.000 7 A43 0.007 0.000 8 A37 0.007 0.000 9 A10 0.007 0.000 10 A18 0.007 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: BKHAMB (size: 44, density: 0.161734)
Rank Agent Value Unscaled Context* 1 A31 0.254 228.985 9.066 2 A7 0.163 147.058 5.295 3 A2 0.145 130.904 4.552 4 A14 0.131 117.847 3.951 5 A16 0.126 113.807 3.765 6 A42 0.104 93.889 2.848 7 A20 0.083 74.633 1.962 8 A18 0.072 65.336 1.534 9 A21 0.044 39.424 0.342 10 A22 0.043 39.000 0.322 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.034 Mean in random network: 0.035 Std.dev: 0.054 Std.dev in random network: 0.024 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): BKHAMB
Rank Agent Value 1 A7 0.705 2 A2 0.593 3 A33 0.487 4 A43 0.485 5 A31 0.483 6 A18 0.429 7 A16 0.285 8 A22 0.203 9 A4 0.179 10 A24 0.149 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): BKHAMB
Rank Agent Value 1 A7 0.705 2 A2 0.593 3 A33 0.487 4 A43 0.485 5 A31 0.483 6 A18 0.429 7 A16 0.285 8 A22 0.203 9 A4 0.179 10 A24 0.149 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): BKHAMB
Rank Agent Value Unscaled 1 A7 0.037 3.423 2 A2 0.037 3.413 3 A31 0.037 3.409 4 A33 0.037 3.390 5 A18 0.037 3.386 6 A43 0.036 3.343 7 A16 0.036 3.297 8 A22 0.035 3.210 9 A4 0.035 3.200 10 A28 0.034 3.133 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): BKHAMB
Rank Agent Value 1 A31 28.000 2 A7 24.000 3 A18 22.000 4 A33 22.000 5 A2 21.000 6 A16 10.000 7 A43 10.000 8 A4 6.000 9 A24 6.000 10 A28 5.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): BKHAMB
Rank Agent Value Unscaled 1 A31 0.581 25.000 2 A7 0.488 21.000 3 A18 0.465 20.000 4 A33 0.465 20.000 5 A2 0.442 19.000 6 A16 0.372 16.000 7 A43 0.302 13.000 8 A4 0.279 12.000 9 A24 0.256 11.000 10 A28 0.256 11.000 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): BKHAMB
Rank Agent Value 1 A9 1.000 2 A11 1.000 3 A29 1.000 4 A35 1.000 5 A40 1.000 6 A27 0.964 7 A26 0.867 8 A10 0.861 9 A37 0.857 10 A39 0.833
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 A31 A31 A7 A7 A7 A31 A7 A7 2 A7 A16 A2 A2 A2 A16 A2 A2 3 A2 A42 A33 A33 A31 A42 A31 A31 4 A14 A14 A43 A43 A33 A14 A33 A33 5 A16 A39 A31 A31 A18 A39 A18 A18 6 A42 A2 A18 A18 A43 A2 A43 A43 7 A20 A43 A16 A16 A16 A43 A16 A16 8 A18 A37 A22 A22 A22 A37 A22 A22 9 A21 A10 A4 A4 A4 A10 A4 A4 10 A22 A18 A24 A24 A24 A18 A24 A24