Network Level Measures
Measure Value Row count 30.000 Column count 30.000 Link count 53.000 Density 0.061 Components of 1 node (isolates) 13 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.767 Characteristic path length 2.189 Clustering coefficient 0.280 Network levels (diameter) 4.000 Network fragmentation 0.687 Krackhardt connectedness 0.313 Krackhardt efficiency 0.883 Krackhardt hierarchy 0.504 Krackhardt upperboundedness 1.000 Degree centralization 0.175 Betweenness centralization 0.084 Closeness centralization 0.047 Eigenvector centralization 0.469 Reciprocal (symmetric)? No (76% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.224 0.061 0.069 Total degree centrality [Unscaled] 0.000 13.000 3.533 3.998 In-degree centrality 0.000 0.207 0.061 0.069 In-degree centrality [Unscaled] 0.000 6.000 1.767 2.011 Out-degree centrality 0.000 0.241 0.061 0.070 Out-degree centrality [Unscaled] 0.000 7.000 1.767 2.028 Eigenvector centrality 0.000 0.608 0.170 0.194 Eigenvector centrality [Unscaled] 0.000 0.430 0.120 0.137 Eigenvector centrality per component 0.000 0.243 0.068 0.078 Closeness centrality 0.033 0.069 0.047 0.016 Closeness centrality [Unscaled] 0.001 0.002 0.002 0.001 In-Closeness centrality 0.033 0.065 0.045 0.011 In-Closeness centrality [Unscaled] 0.001 0.002 0.002 0.000 Betweenness centrality 0.000 0.091 0.010 0.022 Betweenness centrality [Unscaled] 0.000 74.000 7.767 17.854 Hub centrality 0.000 0.604 0.141 0.216 Authority centrality 0.000 0.718 0.134 0.221 Information centrality 0.000 0.099 0.033 0.034 Information centrality [Unscaled] 0.000 1.671 0.562 0.570 Clique membership count 0.000 5.000 0.867 1.310 Simmelian ties 0.000 0.138 0.041 0.054 Simmelian ties [Unscaled] 0.000 4.000 1.200 1.579 Clustering coefficient 0.000 1.000 0.280 0.392
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: 30, density: 0.0609195)
Rank Agent Value Unscaled Context* 1 wadih_el-hage 0.224 13.000 3.738 2 mohamed_owhali 0.190 11.000 2.948 3 khalfan_mohamed 0.172 10.000 2.553 4 ahmed_ghailani 0.155 9.000 2.158 5 bin_laden 0.138 8.000 1.764 6 fahid_msalam 0.138 8.000 1.764 7 mustafa_fadhil 0.138 8.000 1.764 8 swedan_sheikh 0.138 8.000 1.764 9 abdullah_ahmed_abdullah 0.103 6.000 0.974 10 mohammed_odeh 0.086 5.000 0.579 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.061 Mean in random network: 0.061 Std.dev: 0.069 Std.dev in random network: 0.044 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 khalfan_mohamed 0.207 6.000 2 wadih_el-hage 0.207 6.000 3 ahmed_ghailani 0.172 5.000 4 mohamed_owhali 0.172 5.000 5 bin_laden 0.138 4.000 6 fahid_msalam 0.138 4.000 7 mustafa_fadhil 0.138 4.000 8 swedan_sheikh 0.138 4.000 9 abdullah_ahmed_abdullah 0.103 3.000 10 abdal_rahmad 0.069 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 wadih_el-hage 0.241 7.000 2 mohamed_owhali 0.207 6.000 3 ahmed_ghailani 0.138 4.000 4 bin_laden 0.138 4.000 5 fahid_msalam 0.138 4.000 6 khalfan_mohamed 0.138 4.000 7 mustafa_fadhil 0.138 4.000 8 swedan_sheikh 0.138 4.000 9 abdullah_ahmed_abdullah 0.103 3.000 10 mohammed_odeh 0.103 3.000 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: 30, density: 0.0609195)
Rank Agent Value Unscaled Context* 1 khalfan_mohamed 0.608 0.430 1.028 2 mohamed_owhali 0.488 0.345 0.624 3 ahmed_ghailani 0.482 0.341 0.605 4 mustafa_fadhil 0.446 0.316 0.483 5 swedan_sheikh 0.446 0.316 0.483 6 fahid_msalam 0.446 0.316 0.483 7 mohammed_odeh 0.389 0.275 0.290 8 wadih_el-hage 0.386 0.273 0.279 9 bin_laden 0.263 0.186 -0.138 10 abdullah_ahmed_abdullah 0.247 0.175 -0.189 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.170 Mean in random network: 0.303 Std.dev: 0.194 Std.dev in random network: 0.296 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 khalfan_mohamed 0.243 2 mohamed_owhali 0.196 3 ahmed_ghailani 0.193 4 mustafa_fadhil 0.179 5 swedan_sheikh 0.179 6 fahid_msalam 0.179 7 mohammed_odeh 0.156 8 wadih_el-hage 0.155 9 bin_laden 0.105 10 abdullah_ahmed_abdullah 0.099 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: 30, density: 0.0609195)
Rank Agent Value Unscaled Context* 1 mohamed_owhali 0.069 0.002 -3.551 2 wadih_el-hage 0.069 0.002 -3.556 3 mohammed_odeh 0.069 0.002 -3.562 4 bin_laden 0.069 0.002 -3.578 5 fazul_mohammed 0.068 0.002 -3.588 6 abdullah_ahmed_abdullah 0.067 0.002 -3.614 7 abdal_rahmad 0.067 0.002 -3.619 8 ali_mohamed 0.067 0.002 -3.629 9 jihad_mohammed_ali 0.067 0.002 -3.629 10 khalid_al-fawwaz 0.067 0.002 -3.629 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.047 Mean in random network: 0.179 Std.dev: 0.016 Std.dev in random network: 0.031 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 khalfan_mohamed 0.065 0.002 2 ahmed_ghailani 0.064 0.002 3 fahid_msalam 0.063 0.002 4 mustafa_fadhil 0.063 0.002 5 swedan_sheikh 0.063 0.002 6 ahmed_the_german 0.051 0.002 7 mohamed_owhali 0.050 0.002 8 bin_laden 0.049 0.002 9 wadih_el-hage 0.049 0.002 10 mohammed_odeh 0.049 0.002 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: 30, density: 0.0609195)
Rank Agent Value Unscaled Context* 1 mohamed_owhali 0.091 74.000 0.052 2 wadih_el-hage 0.077 62.500 -0.006 3 khalfan_mohamed 0.039 32.000 -0.160 4 bin_laden 0.026 21.500 -0.213 5 mohammed_odeh 0.019 15.500 -0.243 6 abdullah_ahmed_abdullah 0.015 12.000 -0.261 7 fazul_mohammed 0.010 8.000 -0.281 8 ahmed_ghailani 0.009 7.500 -0.283 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.010 Mean in random network: 0.078 Std.dev: 0.022 Std.dev in random network: 0.244 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 fahid_msalam 0.604 2 mustafa_fadhil 0.604 3 swedan_sheikh 0.604 4 ahmed_ghailani 0.591 5 khalfan_mohamed 0.569 6 mohamed_owhali 0.307 7 mohammed_odeh 0.253 8 fazul_mohammed 0.194 9 wadih_el-hage 0.113 10 bin_laden 0.084 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 khalfan_mohamed 0.718 2 ahmed_ghailani 0.624 3 fahid_msalam 0.574 4 mustafa_fadhil 0.574 5 swedan_sheikh 0.574 6 wadih_el-hage 0.176 7 abdullah_ahmed_abdullah 0.149 8 bin_laden 0.139 9 mohammed_odeh 0.102 10 abdal_rahmad 0.084 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Agent x Agent
Rank Agent Value Unscaled 1 mohamed_owhali 0.099 1.671 2 wadih_el-hage 0.094 1.586 3 mohammed_odeh 0.089 1.492 4 bin_laden 0.073 1.226 5 fazul_mohammed 0.071 1.205 6 abdullah_ahmed_abdullah 0.071 1.194 7 khalfan_mohamed 0.056 0.943 8 abdal_rahmad 0.055 0.921 9 ali_mohamed 0.054 0.911 10 khalid_al-fawwaz 0.054 0.911 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 mohamed_owhali 5.000 2 wadih_el-hage 4.000 3 bin_laden 3.000 4 mohammed_odeh 3.000 5 abdullah_ahmed_abdullah 2.000 6 khalfan_mohamed 2.000 7 abdal_rahmad 1.000 8 ahmed_ghailani 1.000 9 ali_mohamed 1.000 10 fahid_msalam 1.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Agent x Agent
Rank Agent Value Unscaled 1 ahmed_ghailani 0.138 4.000 2 fahid_msalam 0.138 4.000 3 khalfan_mohamed 0.138 4.000 4 mustafa_fadhil 0.138 4.000 5 swedan_sheikh 0.138 4.000 6 bin_laden 0.103 3.000 7 wadih_el-hage 0.103 3.000 8 abdal_rahmad 0.069 2.000 9 abdullah_ahmed_abdullah 0.069 2.000 10 ali_mohamed 0.069 2.000 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 abdal_rahmad 1.000 2 ali_mohamed 1.000 3 fahid_msalam 1.000 4 khalid_al-fawwaz 1.000 5 mustafa_fadhil 1.000 6 swedan_sheikh 1.000 7 ahmed_ghailani 0.600 8 khalfan_mohamed 0.433 9 bin_laden 0.417 10 mohammed_odeh 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 mohamed_owhali mohamed_owhali khalfan_mohamed khalfan_mohamed khalfan_mohamed khalfan_mohamed wadih_el-hage wadih_el-hage 2 wadih_el-hage wadih_el-hage mohamed_owhali mohamed_owhali wadih_el-hage ahmed_ghailani mohamed_owhali mohamed_owhali 3 khalfan_mohamed mohammed_odeh ahmed_ghailani ahmed_ghailani ahmed_ghailani fahid_msalam ahmed_ghailani khalfan_mohamed 4 bin_laden bin_laden mustafa_fadhil mustafa_fadhil mohamed_owhali mustafa_fadhil bin_laden ahmed_ghailani 5 mohammed_odeh fazul_mohammed swedan_sheikh swedan_sheikh bin_laden swedan_sheikh fahid_msalam bin_laden 6 abdullah_ahmed_abdullah abdullah_ahmed_abdullah fahid_msalam fahid_msalam fahid_msalam ahmed_the_german khalfan_mohamed fahid_msalam 7 fazul_mohammed abdal_rahmad mohammed_odeh mohammed_odeh mustafa_fadhil mohamed_owhali mustafa_fadhil mustafa_fadhil 8 ahmed_ghailani ali_mohamed wadih_el-hage wadih_el-hage swedan_sheikh bin_laden swedan_sheikh swedan_sheikh 9 abdal_rahmad jihad_mohammed_ali bin_laden bin_laden abdullah_ahmed_abdullah wadih_el-hage abdullah_ahmed_abdullah abdullah_ahmed_abdullah 10 adel_mohammed_abdul_almagid_bary khalid_al-fawwaz abdullah_ahmed_abdullah abdullah_ahmed_abdullah abdal_rahmad mohammed_odeh mohammed_odeh mohammed_odeh