Network Level Measures
Measure Value Row count 18.000 Column count 18.000 Link count 44.000 Density 0.144 Components of 1 node (isolates) 1 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.189 Characteristic path length 2.531 Clustering coefficient 0.387 Network levels (diameter) 5.000 Network fragmentation 0.111 Krackhardt connectedness 0.889 Krackhardt efficiency 0.825 Krackhardt hierarchy 0.504 Krackhardt upperboundedness 0.958 Degree centralization 0.136 Betweenness centralization 0.161 Closeness centralization 0.259 Eigenvector centralization 0.341 Reciprocal (symmetric)? No (18% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.265 0.144 0.076 Total degree centrality [Unscaled] 0.000 9.000 4.889 2.580 In-degree centrality 0.000 0.235 0.144 0.081 In-degree centrality [Unscaled] 0.000 4.000 2.444 1.383 Out-degree centrality 0.000 0.353 0.144 0.090 Out-degree centrality [Unscaled] 0.000 6.000 2.444 1.536 Eigenvector centrality 0.000 0.590 0.287 0.170 Eigenvector centrality [Unscaled] 0.000 0.417 0.203 0.120 Eigenvector centrality per component 0.000 0.394 0.192 0.113 Closeness centrality 0.056 0.262 0.143 0.057 Closeness centrality [Unscaled] 0.003 0.015 0.008 0.003 In-Closeness centrality 0.056 0.340 0.199 0.108 In-Closeness centrality [Unscaled] 0.003 0.020 0.012 0.006 Betweenness centrality 0.000 0.214 0.061 0.069 Betweenness centrality [Unscaled] 0.000 58.100 16.667 18.804 Hub centrality 0.000 0.818 0.218 0.252 Authority centrality 0.000 0.689 0.228 0.243 Information centrality 0.000 0.081 0.056 0.019 Information centrality [Unscaled] 0.000 1.304 0.890 0.311 Clique membership count 0.000 5.000 1.778 1.436 Simmelian ties 0.000 0.176 0.039 0.073 Simmelian ties [Unscaled] 0.000 3.000 0.667 1.247 Clustering coefficient 0.000 1.000 0.387 0.249
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: 18, density: 0.143791)
Rank Agent Value Unscaled Context* 1 Khalfan Khamis Mohamed 0.265 9.000 1.462 2 Ahmed Khalfan Ghalilani 0.235 8.000 1.106 3 Wadih el-Hage 0.235 8.000 1.106 4 Abdullah Ahmed Abdullah 0.206 7.000 0.751 5 Sheik Ahmed Salim Swedan 0.206 7.000 0.751 6 Fahid Mohammed Ally Msalam 0.206 7.000 0.751 7 Al Owali 0.206 7.000 0.751 8 Osama Bin Laden 0.176 6.000 0.395 9 Fazul Abdullah Mohammed 0.147 5.000 0.040 10 Mohammed Odeh 0.147 5.000 0.040 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.144 Mean in random network: 0.144 Std.dev: 0.076 Std.dev in random network: 0.083 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 Fazul Abdullah Mohammed 0.235 4.000 2 Abdullah Ahmed Abdullah 0.235 4.000 3 Ahmed Khalfan Ghalilani 0.235 4.000 4 Sheik Ahmed Salim Swedan 0.235 4.000 5 Fahid Mohammed Ally Msalam 0.235 4.000 6 Wadih el-Hage 0.235 4.000 7 Khalfan Khamis Mohamed 0.176 3.000 8 Al Owali 0.176 3.000 9 Mohammed Odeh 0.176 3.000 10 Muhammed Atef 0.118 2.000 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 Khalfan Khamis Mohamed 0.353 6.000 2 Ahmed Khalfan Ghalilani 0.235 4.000 3 Osama Bin Laden 0.235 4.000 4 Wadih el-Hage 0.235 4.000 5 Al Owali 0.235 4.000 6 Abdullah Ahmed Abdullah 0.176 3.000 7 Sheik Ahmed Salim Swedan 0.176 3.000 8 Fahid Mohammed Ally Msalam 0.176 3.000 9 Mustafa Mohamed Fadhil 0.176 3.000 10 Mohammed Odeh 0.118 2.000 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: 18, density: 0.143791)
Rank Agent Value Unscaled Context* 1 Wadih el-Hage 0.590 0.417 0.587 2 Al Owali 0.587 0.415 0.579 3 Abdullah Ahmed Abdullah 0.488 0.345 0.273 4 Mohammed Odeh 0.472 0.334 0.224 5 Abdel Rahman 0.412 0.292 0.041 6 Fazul Abdullah Mohammed 0.371 0.262 -0.087 7 Khalfan Khamis Mohamed 0.367 0.260 -0.097 8 Osama Bin Laden 0.342 0.242 -0.175 9 Ahmed Khalfan Ghalilani 0.256 0.181 -0.440 10 Ali Mohammed 0.204 0.144 -0.601 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.287 Mean in random network: 0.399 Std.dev: 0.170 Std.dev in random network: 0.325 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 Wadih el-Hage 0.394 2 Al Owali 0.392 3 Abdullah Ahmed Abdullah 0.326 4 Mohammed Odeh 0.315 5 Abdel Rahman 0.275 6 Fazul Abdullah Mohammed 0.248 7 Khalfan Khamis Mohamed 0.245 8 Osama Bin Laden 0.229 9 Ahmed Khalfan Ghalilani 0.171 10 Ali Mohammed 0.136 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: 18, density: 0.143791)
Rank Agent Value Unscaled Context* 1 Khalfan Khamis Mohamed 0.262 0.015 -1.501 2 Ahmed Khalfan Ghalilani 0.233 0.014 -2.044 3 Sheik Ahmed Salim Swedan 0.227 0.013 -2.161 4 Fahid Mohammed Ally Msalam 0.227 0.013 -2.161 5 Mustafa Mohamed Fadhil 0.202 0.012 -2.621 6 Ahmed the German 0.121 0.007 -4.155 7 Al Owali 0.120 0.007 -4.187 8 Osama Bin Laden 0.118 0.007 -4.219 9 Wadih el-Hage 0.117 0.007 -4.234 10 Abdullah Ahmed Abdullah 0.116 0.007 -4.264 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.143 Mean in random network: 0.341 Std.dev: 0.057 Std.dev in random network: 0.053 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 Fazul Abdullah Mohammed 0.340 0.020 2 Abdullah Ahmed Abdullah 0.315 0.019 3 Wadih el-Hage 0.315 0.019 4 Al Owali 0.298 0.018 5 Mohammed Odeh 0.298 0.018 6 Abdel Rahman 0.288 0.017 7 Osama Bin Laden 0.274 0.016 8 Ali Mohammed 0.254 0.015 9 Khlid Al Fawaz 0.250 0.015 10 Muhammed Atef 0.246 0.014 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: 18, density: 0.143791)
Rank Agent Value Unscaled Context* 1 Al Owali 0.214 58.100 1.740 2 Wadih el-Hage 0.179 48.733 1.228 3 Osama Bin Laden 0.176 48.000 1.188 4 Khalfan Khamis Mohamed 0.143 39.000 0.696 5 Abdullah Ahmed Abdullah 0.116 31.500 0.286 6 Ali Mohammed 0.063 17.000 -0.506 7 Mohammed Odeh 0.047 12.700 -0.741 8 Ahmed Khalfan Ghalilani 0.037 10.033 -0.887 9 Khlid Al Fawaz 0.037 10.000 -0.889 10 Muhammed Atef 0.033 9.000 -0.943 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.061 Mean in random network: 0.097 Std.dev: 0.069 Std.dev in random network: 0.067 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 Khalfan Khamis Mohamed 0.818 2 Ahmed Khalfan Ghalilani 0.597 3 Mustafa Mohamed Fadhil 0.567 4 Sheik Ahmed Salim Swedan 0.499 5 Fahid Mohammed Ally Msalam 0.499 6 Abdullah Ahmed Abdullah 0.279 7 Al Owali 0.153 8 Wadih el-Hage 0.151 9 Mohammed Odeh 0.120 10 Abdel Rahman 0.118 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 Sheik Ahmed Salim Swedan 0.689 2 Fahid Mohammed Ally Msalam 0.689 3 Ahmed Khalfan Ghalilani 0.662 4 Khalfan Khamis Mohamed 0.443 5 Al Owali 0.338 6 Mohammed Odeh 0.338 7 Fazul Abdullah Mohammed 0.328 8 Mustafa Mohamed Fadhil 0.227 9 Wadih el-Hage 0.092 10 Abdullah Ahmed Abdullah 0.088 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 Osama Bin Laden 0.081 1.304 2 Wadih el-Hage 0.080 1.289 3 Al Owali 0.079 1.269 4 Khalfan Khamis Mohamed 0.077 1.239 5 Mustafa Mohamed Fadhil 0.068 1.087 6 Ahmed Khalfan Ghalilani 0.065 1.046 7 Abdullah Ahmed Abdullah 0.064 1.029 8 Mohammed Odeh 0.061 0.972 9 Abdel Rahman 0.059 0.941 10 Sheik Ahmed Salim Swedan 0.054 0.858 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 Osama Bin Laden 5.000 2 Wadih el-Hage 5.000 3 Al Owali 4.000 4 Muhammed Atef 2.000 5 Abdullah Ahmed Abdullah 2.000 6 Khalfan Khamis Mohamed 2.000 7 Ali Mohammed 2.000 8 Mohammed Odeh 2.000 9 Fazul Abdullah Mohammed 1.000 10 Ahmed Khalfan Ghalilani 1.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 Khalfan Khamis Mohamed 0.176 3.000 2 Ahmed Khalfan Ghalilani 0.176 3.000 3 Sheik Ahmed Salim Swedan 0.176 3.000 4 Fahid Mohammed Ally Msalam 0.176 3.000 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 Mustafa Mohamed Fadhil 1.000 2 Sheik Ahmed Salim Swedan 0.750 3 Fahid Mohammed Ally Msalam 0.750 4 Anas al-Liby 0.500 5 Khlid Al Fawaz 0.500 6 Abdel Rahman 0.500 7 Ahmed Khalfan Ghalilani 0.450 8 Mohammed Odeh 0.350 9 Muhammed Atef 0.333 10 Khalfan Khamis Mohamed 0.333
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 Al Owali Khalfan Khamis Mohamed Wadih el-Hage Wadih el-Hage Fazul Abdullah Mohammed Fazul Abdullah Mohammed Khalfan Khamis Mohamed Khalfan Khamis Mohamed 2 Wadih el-Hage Ahmed Khalfan Ghalilani Al Owali Al Owali Abdullah Ahmed Abdullah Abdullah Ahmed Abdullah Ahmed Khalfan Ghalilani Ahmed Khalfan Ghalilani 3 Osama Bin Laden Sheik Ahmed Salim Swedan Abdullah Ahmed Abdullah Abdullah Ahmed Abdullah Ahmed Khalfan Ghalilani Wadih el-Hage Osama Bin Laden Wadih el-Hage 4 Khalfan Khamis Mohamed Fahid Mohammed Ally Msalam Mohammed Odeh Mohammed Odeh Sheik Ahmed Salim Swedan Al Owali Wadih el-Hage Abdullah Ahmed Abdullah 5 Abdullah Ahmed Abdullah Mustafa Mohamed Fadhil Abdel Rahman Abdel Rahman Fahid Mohammed Ally Msalam Mohammed Odeh Al Owali Sheik Ahmed Salim Swedan 6 Ali Mohammed Ahmed the German Fazul Abdullah Mohammed Fazul Abdullah Mohammed Wadih el-Hage Abdel Rahman Abdullah Ahmed Abdullah Fahid Mohammed Ally Msalam 7 Mohammed Odeh Al Owali Khalfan Khamis Mohamed Khalfan Khamis Mohamed Khalfan Khamis Mohamed Osama Bin Laden Sheik Ahmed Salim Swedan Al Owali 8 Ahmed Khalfan Ghalilani Osama Bin Laden Osama Bin Laden Osama Bin Laden Al Owali Ali Mohammed Fahid Mohammed Ally Msalam Osama Bin Laden 9 Khlid Al Fawaz Wadih el-Hage Ahmed Khalfan Ghalilani Ahmed Khalfan Ghalilani Mohammed Odeh Khlid Al Fawaz Mustafa Mohamed Fadhil Fazul Abdullah Mohammed 10 Muhammed Atef Abdullah Ahmed Abdullah Ali Mohammed Ali Mohammed Muhammed Atef Muhammed Atef Mohammed Odeh Mohammed Odeh