Network Level Measures
Measure Value Row count 32.000 Column count 32.000 Link count 162.000 Density 0.163 Components of 1 node (isolates) 5 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.800 Characteristic path length 2.485 Clustering coefficient 0.405 Network levels (diameter) 7.000 Network fragmentation 0.292 Krackhardt connectedness 0.708 Krackhardt efficiency 0.803 Krackhardt hierarchy 0.275 Krackhardt upperboundedness 0.994 Degree centralization 0.112 Betweenness centralization 0.143 Closeness centralization 0.022 Eigenvector centralization 0.376 Reciprocal (symmetric)? No (80% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.157 0.052 0.047 Total degree centrality [Unscaled] 0.000 39.000 12.938 11.656 In-degree centrality 0.000 0.177 0.052 0.049 In-degree centrality [Unscaled] 0.000 22.000 6.469 6.088 Out-degree centrality 0.000 0.169 0.052 0.048 Out-degree centrality [Unscaled] 0.000 21.000 6.469 5.989 Eigenvector centrality 0.000 0.539 0.186 0.167 Eigenvector centrality [Unscaled] 0.000 0.381 0.132 0.118 Eigenvector centrality per component 0.000 0.322 0.111 0.099 Closeness centrality 0.008 0.036 0.025 0.008 Closeness centrality [Unscaled] 0.000 0.001 0.001 0.000 In-Closeness centrality 0.008 0.041 0.030 0.013 In-Closeness centrality [Unscaled] 0.000 0.001 0.001 0.000 Betweenness centrality 0.000 0.167 0.028 0.037 Betweenness centrality [Unscaled] 0.000 154.855 25.756 34.740 Hub centrality 0.000 0.623 0.178 0.175 Authority centrality 0.000 0.578 0.177 0.176 Information centrality 0.000 0.057 0.031 0.019 Information centrality [Unscaled] 0.000 3.573 1.976 1.204 Clique membership count 0.000 12.000 3.219 3.342 Simmelian ties 0.000 0.419 0.139 0.136 Simmelian ties [Unscaled] 0.000 13.000 4.313 4.209 Clustering coefficient 0.000 1.000 0.405 0.300
Key Nodes
This chart shows the Location that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Location 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: Location x Location (size: 32, density: 0.163306)
Rank Location Value Unscaled Context* 1 pakistan 0.157 39.000 -0.093 2 airport 0.153 38.000 -0.154 3 afghanistan 0.137 34.000 -0.401 4 yemen 0.117 29.000 -0.710 5 usa 0.105 26.000 -0.895 6 africa 0.105 26.000 -0.895 7 somalia 0.093 23.000 -1.080 8 harbor 0.093 23.000 -1.080 9 israel 0.081 20.000 -1.265 10 egypt 0.081 20.000 -1.265 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.052 Mean in random network: 0.163 Std.dev: 0.047 Std.dev in random network: 0.065 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): Location x Location
Rank Location Value Unscaled 1 airport 0.177 22.000 2 pakistan 0.145 18.000 3 afghanistan 0.137 17.000 4 harbor 0.129 16.000 5 yemen 0.113 14.000 6 africa 0.113 14.000 7 somalia 0.089 11.000 8 israel 0.081 10.000 9 egypt 0.081 10.000 10 gulf 0.073 9.000 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): Location x Location
Rank Location Value Unscaled 1 pakistan 0.169 21.000 2 usa 0.153 19.000 3 afghanistan 0.137 17.000 4 airport 0.129 16.000 5 yemen 0.121 15.000 6 somalia 0.097 12.000 7 africa 0.097 12.000 8 israel 0.081 10.000 9 egypt 0.081 10.000 10 europe 0.065 8.000 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: Location x Location (size: 32, density: 0.163306)
Rank Location Value Unscaled Context* 1 airport 0.539 0.381 0.197 2 pakistan 0.533 0.377 0.174 3 afghanistan 0.458 0.324 -0.092 4 harbor 0.433 0.306 -0.181 5 usa 0.415 0.294 -0.244 6 yemen 0.383 0.271 -0.359 7 africa 0.348 0.246 -0.484 8 somalia 0.312 0.221 -0.609 9 israel 0.267 0.189 -0.770 10 egypt 0.259 0.183 -0.800 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.186 Mean in random network: 0.484 Std.dev: 0.167 Std.dev in random network: 0.282 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): Location x Location
Rank Location Value 1 airport 0.322 2 pakistan 0.318 3 afghanistan 0.273 4 harbor 0.258 5 usa 0.248 6 yemen 0.228 7 africa 0.207 8 somalia 0.186 9 israel 0.159 10 egypt 0.154 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: Location x Location (size: 32, density: 0.163306)
Rank Location Value Unscaled Context* 1 nairobi 0.036 0.001 -6.386 2 kenya 0.032 0.001 -6.451 3 dar_es_salaam 0.032 0.001 -6.459 4 airport 0.029 0.001 -6.502 5 israel 0.029 0.001 -6.503 6 egypt 0.029 0.001 -6.503 7 lebanon 0.029 0.001 -6.504 8 saudi_arabia 0.029 0.001 -6.505 9 africa 0.029 0.001 -6.505 10 asia 0.029 0.001 -6.505 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.025 Mean in random network: 0.409 Std.dev: 0.008 Std.dev in random network: 0.058 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): Location x Location
Rank Location Value Unscaled 1 ocean 0.041 0.001 2 africa 0.038 0.001 3 israel 0.038 0.001 4 airport 0.038 0.001 5 egypt 0.038 0.001 6 gulf 0.038 0.001 7 asia 0.038 0.001 8 lebanon 0.038 0.001 9 pakistan 0.038 0.001 10 saudi_arabia 0.038 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: Location x Location (size: 32, density: 0.163306)
Rank Location Value Unscaled Context* 1 airport 0.167 154.855 3.424 2 new_york 0.115 106.622 1.900 3 africa 0.096 89.708 1.366 4 yemen 0.070 64.750 0.577 5 israel 0.067 62.211 0.497 6 egypt 0.046 42.357 -0.131 7 usa 0.031 29.256 -0.545 8 lebanon 0.031 28.820 -0.559 9 pakistan 0.028 26.312 -0.638 10 aden_harbor 0.028 25.861 -0.652 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.028 Mean in random network: 0.050 Std.dev: 0.037 Std.dev in random network: 0.034 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): Location x Location
Rank Location Value 1 pakistan 0.623 2 afghanistan 0.507 3 usa 0.477 4 yemen 0.435 5 airport 0.425 6 somalia 0.348 7 africa 0.342 8 israel 0.305 9 egypt 0.294 10 europe 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): Location x Location
Rank Location Value 1 airport 0.578 2 afghanistan 0.517 3 pakistan 0.495 4 harbor 0.483 5 africa 0.397 6 yemen 0.375 7 somalia 0.351 8 egypt 0.282 9 israel 0.269 10 gulf 0.264 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Location x Location
Rank Location Value Unscaled 1 pakistan 0.057 3.573 2 usa 0.054 3.433 3 airport 0.054 3.421 4 afghanistan 0.054 3.393 5 yemen 0.053 3.326 6 africa 0.050 3.192 7 somalia 0.050 3.137 8 israel 0.047 2.991 9 egypt 0.047 2.978 10 europe 0.045 2.874 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): Location x Location
Rank Location Value 1 pakistan 12.000 2 airport 12.000 3 africa 8.000 4 israel 7.000 5 yemen 7.000 6 egypt 7.000 7 afghanistan 6.000 8 harbor 6.000 9 usa 4.000 10 somalia 4.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Location x Location
Rank Location Value Unscaled 1 airport 0.419 13.000 2 pakistan 0.387 12.000 3 afghanistan 0.355 11.000 4 israel 0.323 10.000 5 africa 0.323 10.000 6 egypt 0.323 10.000 7 yemen 0.290 9.000 8 somalia 0.258 8.000 9 asia 0.258 8.000 10 lebanon 0.226 7.000 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): Location x Location
Rank Location Value 1 north_america 1.000 2 manhattan 1.000 3 asia 0.768 4 gulf 0.694 5 egypt 0.678 6 lebanon 0.667 7 saudi_arabia 0.643 8 somalia 0.639 9 israel 0.633 10 afghanistan 0.627
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 airport nairobi airport airport airport ocean pakistan pakistan 2 new_york kenya pakistan pakistan pakistan africa usa airport 3 africa dar_es_salaam afghanistan afghanistan afghanistan israel afghanistan afghanistan 4 yemen airport harbor harbor harbor airport airport yemen 5 israel israel usa usa yemen egypt yemen usa 6 egypt egypt yemen yemen africa gulf somalia africa 7 usa lebanon africa africa somalia asia africa somalia 8 lebanon saudi_arabia somalia somalia israel lebanon israel harbor 9 pakistan africa israel israel egypt pakistan egypt israel 10 aden_harbor asia egypt egypt gulf saudi_arabia europe egypt