Network Level Measures
Measure Value Row count 16.000 Column count 16.000 Link count 37.000 Density 0.308 Components of 1 node (isolates) 0 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 1.000 Characteristic path length 2.067 Clustering coefficient 0.449 Network levels (diameter) 5.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.790 Krackhardt hierarchy 0.000 Krackhardt upperboundedness 1.000 Degree centralization 0.257 Betweenness centralization 0.146 Closeness centralization 0.327 Eigenvector centralization 0.296 Reciprocal (symmetric)? Yes
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.067 0.533 0.308 0.137 Total degree centrality [Unscaled] 1.000 8.000 4.625 2.058 In-degree centrality 0.067 0.533 0.308 0.137 In-degree centrality [Unscaled] 1.000 8.000 4.625 2.058 Out-degree centrality 0.067 0.533 0.308 0.137 Out-degree centrality [Unscaled] 1.000 8.000 4.625 2.058 Eigenvector centrality 0.026 0.575 0.316 0.159 Eigenvector centrality [Unscaled] 0.018 0.407 0.223 0.112 Eigenvector centrality per component 0.018 0.407 0.223 0.112 Closeness centrality 0.288 0.652 0.504 0.092 Closeness centrality [Unscaled] 0.019 0.043 0.034 0.006 In-Closeness centrality 0.288 0.652 0.504 0.092 In-Closeness centrality [Unscaled] 0.019 0.043 0.034 0.006 Betweenness centrality 0.000 0.213 0.076 0.073 Betweenness centrality [Unscaled] 0.000 22.396 8.000 7.624 Hub centrality 0.026 0.575 0.316 0.159 Authority centrality 0.026 0.575 0.316 0.159 Information centrality 0.024 0.082 0.063 0.016 Information centrality [Unscaled] 0.569 1.970 1.502 0.392 Clique membership count 0.000 5.000 2.375 1.495 Simmelian ties 0.000 0.533 0.283 0.155 Simmelian ties [Unscaled] 0.000 8.000 4.250 2.332 Clustering coefficient 0.000 1.000 0.449 0.243
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: agent x agent (size: 16, density: 0.308333)
Rank Agent Value Unscaled Context* 1 Technical Lead 0.533 8.000 1.949 2 Software Engineer 2 0.533 8.000 1.949 3 Application Architect 0.467 7.000 1.371 4 Project Manager 0.400 6.000 0.794 5 Design Lead 0.400 6.000 0.794 6 Web Developer 0.400 6.000 0.794 7 Art Director 0.333 5.000 0.217 8 Data Architect 0.267 4.000 -0.361 9 Designer 0.267 4.000 -0.361 10 Business Analyst 1 0.267 4.000 -0.361 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.308 Mean in random network: 0.308 Std.dev: 0.137 Std.dev in random network: 0.115 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): agent x agent
Rank Agent Value Unscaled 1 Technical Lead 0.533 8.000 2 Software Engineer 2 0.533 8.000 3 Application Architect 0.467 7.000 4 Project Manager 0.400 6.000 5 Design Lead 0.400 6.000 6 Web Developer 0.400 6.000 7 Art Director 0.333 5.000 8 Data Architect 0.267 4.000 9 Designer 0.267 4.000 10 Business Analyst 1 0.267 4.000 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): agent x agent
Rank Agent Value Unscaled 1 Technical Lead 0.533 8.000 2 Software Engineer 2 0.533 8.000 3 Application Architect 0.467 7.000 4 Project Manager 0.400 6.000 5 Design Lead 0.400 6.000 6 Web Developer 0.400 6.000 7 Art Director 0.333 5.000 8 Data Architect 0.267 4.000 9 Designer 0.267 4.000 10 Business Analyst 1 0.267 4.000 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: agent x agent (size: 16, density: 0.308333)
Rank Agent Value Unscaled Context* 1 Technical Lead 0.575 0.407 0.009 2 Software Engineer 2 0.530 0.375 -0.153 3 Project Manager 0.500 0.354 -0.259 4 Web Developer 0.480 0.340 -0.330 5 Application Architect 0.428 0.303 -0.516 6 Design Lead 0.422 0.299 -0.536 7 Art Director 0.378 0.267 -0.696 8 Designer 0.296 0.210 -0.986 9 Data Architect 0.262 0.185 -1.110 10 Software Engineer 4 0.250 0.177 -1.151 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.316 Mean in random network: 0.573 Std.dev: 0.159 Std.dev in random network: 0.280 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): agent x agent
Rank Agent Value 1 Technical Lead 0.407 2 Software Engineer 2 0.375 3 Project Manager 0.354 4 Web Developer 0.340 5 Application Architect 0.303 6 Design Lead 0.299 7 Art Director 0.267 8 Designer 0.210 9 Data Architect 0.185 10 Software Engineer 4 0.177 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: agent x agent (size: 16, density: 0.308333)
Rank Agent Value Unscaled Context* 1 Technical Lead 0.652 0.043 0.818 2 Project Manager 0.600 0.040 0.115 3 Software Engineer 2 0.600 0.040 0.115 4 Application Architect 0.577 0.038 -0.197 5 Web Developer 0.577 0.038 -0.197 6 Design Lead 0.556 0.037 -0.485 7 Designer 0.536 0.036 -0.752 8 Art Director 0.517 0.034 -1.001 9 Data Architect 0.517 0.034 -1.001 10 Business Analyst 2 0.500 0.033 -1.233 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.504 Mean in random network: 0.592 Std.dev: 0.092 Std.dev in random network: 0.074 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): agent x agent
Rank Agent Value Unscaled 1 Technical Lead 0.652 0.043 2 Project Manager 0.600 0.040 3 Software Engineer 2 0.600 0.040 4 Application Architect 0.577 0.038 5 Web Developer 0.577 0.038 6 Design Lead 0.556 0.037 7 Designer 0.536 0.036 8 Art Director 0.517 0.034 9 Data Architect 0.517 0.034 10 Business Analyst 2 0.500 0.033 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: agent x agent (size: 16, density: 0.308333)
Rank Agent Value Unscaled Context* 1 Technical Lead 0.213 22.396 4.033 2 Application Architect 0.199 20.862 3.632 3 Software Engineer 2 0.177 18.616 3.043 4 Design Lead 0.149 15.620 2.258 5 Interactive Lead 0.133 14.000 1.834 6 Art Director 0.092 9.689 0.704 7 Project Manager 0.082 8.659 0.434 8 Designer 0.047 4.967 -0.533 9 Web Developer 0.038 3.955 -0.798 10 Business Analyst 1 0.025 2.667 -1.136 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.076 Mean in random network: 0.067 Std.dev: 0.073 Std.dev in random network: 0.036 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): agent x agent
Rank Agent Value 1 Technical Lead 0.575 2 Software Engineer 2 0.530 3 Project Manager 0.500 4 Web Developer 0.480 5 Application Architect 0.428 6 Design Lead 0.422 7 Art Director 0.378 8 Designer 0.296 9 Data Architect 0.262 10 Software Engineer 4 0.250 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): agent x agent
Rank Agent Value 1 Technical Lead 0.575 2 Software Engineer 2 0.530 3 Project Manager 0.500 4 Web Developer 0.480 5 Application Architect 0.428 6 Design Lead 0.422 7 Art Director 0.378 8 Designer 0.296 9 Data Architect 0.262 10 Software Engineer 4 0.250 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 Technical Lead 0.082 1.970 2 Software Engineer 2 0.081 1.943 3 Application Architect 0.076 1.829 4 Project Manager 0.075 1.807 5 Web Developer 0.074 1.779 6 Design Lead 0.073 1.759 7 Art Director 0.069 1.647 8 Designer 0.065 1.551 9 Data Architect 0.063 1.517 10 Business Analyst 1 0.063 1.506 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): agent x agent
Rank Agent Value 1 Technical Lead 5.000 2 Software Engineer 2 5.000 3 Web Developer 4.000 4 Project Manager 3.000 5 Design Lead 3.000 6 Data Architect 3.000 7 Application Architect 3.000 8 Designer 3.000 9 Art Director 2.000 10 Business Analyst 1 2.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 Technical Lead 0.533 8.000 2 Software Engineer 2 0.533 8.000 3 Project Manager 0.400 6.000 4 Design Lead 0.400 6.000 5 Application Architect 0.400 6.000 6 Web Developer 0.400 6.000 7 Art Director 0.333 5.000 8 Data Architect 0.267 4.000 9 Designer 0.267 4.000 10 Business Analyst 1 0.267 4.000 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): agent x agent
Rank Agent Value 1 Software Engineer 1 1.000 2 Art Director 0.700 3 Project Manager 0.667 4 Web Developer 0.667 5 Design Lead 0.533 6 Data Architect 0.500 7 Designer 0.500 8 Software Engineer 4 0.500 9 Technical Lead 0.429 10 Software Engineer 2 0.357
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 Technical Lead Technical Lead Technical Lead Technical Lead Technical Lead Technical Lead Technical Lead Technical Lead 2 Application Architect Project Manager Software Engineer 2 Software Engineer 2 Software Engineer 2 Project Manager Software Engineer 2 Software Engineer 2 3 Software Engineer 2 Software Engineer 2 Project Manager Project Manager Application Architect Software Engineer 2 Application Architect Application Architect 4 Design Lead Application Architect Web Developer Web Developer Project Manager Application Architect Project Manager Project Manager 5 Interactive Lead Web Developer Application Architect Application Architect Design Lead Web Developer Design Lead Design Lead 6 Art Director Design Lead Design Lead Design Lead Web Developer Design Lead Web Developer Web Developer 7 Project Manager Designer Art Director Art Director Art Director Designer Art Director Art Director 8 Designer Art Director Designer Designer Data Architect Art Director Data Architect Data Architect 9 Web Developer Data Architect Data Architect Data Architect Designer Data Architect Designer Designer 10 Business Analyst 1 Business Analyst 2 Software Engineer 4 Software Engineer 4 Business Analyst 1 Business Analyst 2 Business Analyst 1 Business Analyst 1