Network Level Measures
Measure Value Row count 27.000 Column count 27.000 Link count 53.000 Density 0.075 Components of 1 node (isolates) 10 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.767 Characteristic path length 2.189 Clustering coefficient 0.311 Network levels (diameter) 4.000 Network fragmentation 0.613 Krackhardt connectedness 0.387 Krackhardt efficiency 0.883 Krackhardt hierarchy 0.504 Krackhardt upperboundedness 0.958 Degree centralization 0.188 Betweenness centralization 0.104 Closeness centralization 0.056 Eigenvector centralization 0.452 Reciprocal (symmetric)? No (76% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.250 0.075 0.077 Total degree centrality [Unscaled] 0.000 13.000 3.926 4.027 In-degree centrality 0.000 0.269 0.075 0.079 In-degree centrality [Unscaled] 0.000 7.000 1.963 2.045 Out-degree centrality 0.000 0.231 0.075 0.078 Out-degree centrality [Unscaled] 0.000 6.000 1.963 2.027 Eigenvector centrality 0.000 0.608 0.189 0.196 Eigenvector centrality [Unscaled] 0.000 0.430 0.134 0.138 Eigenvector centrality per component 0.000 0.271 0.084 0.087 Closeness centrality 0.037 0.080 0.054 0.015 Closeness centrality [Unscaled] 0.001 0.003 0.002 0.001 In-Closeness centrality 0.037 0.087 0.058 0.023 In-Closeness centrality [Unscaled] 0.001 0.003 0.002 0.001 Betweenness centrality 0.000 0.114 0.013 0.029 Betweenness centrality [Unscaled] 0.000 74.000 8.630 18.621 Hub centrality 0.000 0.718 0.149 0.228 Authority centrality 0.000 0.604 0.157 0.223 Information centrality 0.000 0.082 0.037 0.031 Information centrality [Unscaled] 0.000 1.542 0.698 0.582 Clique membership count 0.000 5.000 0.963 1.347 Simmelian ties 0.000 0.154 0.051 0.062 Simmelian ties [Unscaled] 0.000 4.000 1.333 1.610 Clustering coefficient 0.000 1.000 0.311 0.401
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: 27, density: 0.0754986)
Rank Agent Value Unscaled Context* 1 wadih_el-hage 0.250 13.000 3.432 2 mohamed_owhali 0.212 11.000 2.676 3 khalfan_mohamed 0.192 10.000 2.297 4 ahmed_ghailani 0.173 9.000 1.919 5 bin_laden 0.154 8.000 1.541 6 swedan_sheikh 0.154 8.000 1.541 7 mustafa_fadhil 0.154 8.000 1.541 8 fahid_msalam 0.154 8.000 1.541 9 abdullah_ahmed_abdullah 0.115 6.000 0.784 10 mohammed_odeh 0.096 5.000 0.406 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.075 Mean in random network: 0.075 Std.dev: 0.077 Std.dev in random network: 0.051 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 wadih_el-hage 0.269 7.000 2 mohamed_owhali 0.231 6.000 3 khalfan_mohamed 0.154 4.000 4 bin_laden 0.154 4.000 5 ahmed_ghailani 0.154 4.000 6 swedan_sheikh 0.154 4.000 7 mustafa_fadhil 0.154 4.000 8 fahid_msalam 0.154 4.000 9 mohammed_odeh 0.115 3.000 10 abdullah_ahmed_abdullah 0.115 3.000 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_mohamed 0.231 6.000 2 wadih_el-hage 0.231 6.000 3 mohamed_owhali 0.192 5.000 4 ahmed_ghailani 0.192 5.000 5 bin_laden 0.154 4.000 6 swedan_sheikh 0.154 4.000 7 mustafa_fadhil 0.154 4.000 8 fahid_msalam 0.154 4.000 9 abdullah_ahmed_abdullah 0.115 3.000 10 mohammed_odeh 0.077 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: 27, density: 0.0754986)
Rank Agent Value Unscaled Context* 1 khalfan_mohamed 0.608 0.430 0.935 2 mohamed_owhali 0.488 0.345 0.533 3 ahmed_ghailani 0.482 0.341 0.513 4 swedan_sheikh 0.446 0.316 0.392 5 mustafa_fadhil 0.446 0.316 0.392 6 fahid_msalam 0.446 0.316 0.392 7 mohammed_odeh 0.389 0.275 0.200 8 wadih_el-hage 0.386 0.273 0.189 9 bin_laden 0.263 0.186 -0.226 10 abdullah_ahmed_abdullah 0.247 0.175 -0.277 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.189 Mean in random network: 0.330 Std.dev: 0.196 Std.dev in random network: 0.297 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 0.271 2 mohamed_owhali 0.217 3 ahmed_ghailani 0.215 4 swedan_sheikh 0.199 5 mustafa_fadhil 0.199 6 fahid_msalam 0.199 7 mohammed_odeh 0.173 8 wadih_el-hage 0.172 9 bin_laden 0.117 10 abdullah_ahmed_abdullah 0.110 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: 27, density: 0.0754986)
Rank Agent Value Unscaled Context* 1 khalfan_mohamed 0.080 0.003 -3.233 2 ahmed_ghailani 0.079 0.003 -3.261 3 swedan_sheikh 0.078 0.003 -3.289 4 mustafa_fadhil 0.078 0.003 -3.289 5 fahid_msalam 0.078 0.003 -3.289 6 ahmed_the_german 0.059 0.002 -3.711 7 mohamed_owhali 0.058 0.002 -3.742 8 bin_laden 0.058 0.002 -3.745 9 wadih_el-hage 0.058 0.002 -3.748 10 mohammed_odeh 0.058 0.002 -3.751 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.054 Mean in random network: 0.221 Std.dev: 0.015 Std.dev in random network: 0.043 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 mohamed_owhali 0.087 0.003 2 wadih_el-hage 0.087 0.003 3 mohammed_odeh 0.087 0.003 4 bin_laden 0.086 0.003 5 fazul_mohammed 0.085 0.003 6 abdullah_ahmed_abdullah 0.084 0.003 7 abdal_rahmad 0.084 0.003 8 ali_mohamed 0.083 0.003 9 khalid_al-fawwaz 0.083 0.003 10 jihad_mohammed_ali 0.083 0.003 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: 27, density: 0.0754986)
Rank Agent Value Unscaled Context* 1 mohamed_owhali 0.114 74.000 0.186 2 wadih_el-hage 0.096 62.500 0.086 3 khalfan_mohamed 0.049 32.000 -0.180 4 bin_laden 0.033 21.500 -0.272 5 mohammed_odeh 0.024 15.500 -0.324 6 abdullah_ahmed_abdullah 0.018 12.000 -0.355 7 fazul_mohammed 0.012 8.000 -0.389 8 ahmed_ghailani 0.012 7.500 -0.394 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.013 Mean in random network: 0.081 Std.dev: 0.029 Std.dev in random network: 0.176 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_mohamed 0.718 2 ahmed_ghailani 0.624 3 swedan_sheikh 0.574 4 mustafa_fadhil 0.574 5 fahid_msalam 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 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 swedan_sheikh 0.604 2 mustafa_fadhil 0.604 3 fahid_msalam 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 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 khalfan_mohamed 0.082 1.542 2 mohamed_owhali 0.081 1.530 3 wadih_el-hage 0.081 1.520 4 ahmed_ghailani 0.072 1.355 5 bin_laden 0.071 1.341 6 abdullah_ahmed_abdullah 0.066 1.240 7 mohammed_odeh 0.063 1.197 8 swedan_sheikh 0.060 1.130 9 mustafa_fadhil 0.060 1.130 10 fahid_msalam 0.060 1.130 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 mohammed_odeh 3.000 4 bin_laden 3.000 5 khalfan_mohamed 2.000 6 abdullah_ahmed_abdullah 2.000 7 ali_mohamed 1.000 8 ahmed_ghailani 1.000 9 khalid_al-fawwaz 1.000 10 abdal_rahmad 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_mohamed 0.154 4.000 2 ahmed_ghailani 0.154 4.000 3 swedan_sheikh 0.154 4.000 4 mustafa_fadhil 0.154 4.000 5 fahid_msalam 0.154 4.000 6 wadih_el-hage 0.115 3.000 7 bin_laden 0.115 3.000 8 mohamed_owhali 0.077 2.000 9 ali_mohamed 0.077 2.000 10 khalid_al-fawwaz 0.077 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 ali_mohamed 1.000 2 khalid_al-fawwaz 1.000 3 abdal_rahmad 1.000 4 swedan_sheikh 1.000 5 mustafa_fadhil 1.000 6 fahid_msalam 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 khalfan_mohamed khalfan_mohamed khalfan_mohamed wadih_el-hage mohamed_owhali khalfan_mohamed wadih_el-hage 2 wadih_el-hage ahmed_ghailani mohamed_owhali mohamed_owhali mohamed_owhali wadih_el-hage wadih_el-hage mohamed_owhali 3 khalfan_mohamed swedan_sheikh ahmed_ghailani ahmed_ghailani khalfan_mohamed mohammed_odeh mohamed_owhali khalfan_mohamed 4 bin_laden mustafa_fadhil swedan_sheikh swedan_sheikh bin_laden bin_laden ahmed_ghailani ahmed_ghailani 5 mohammed_odeh fahid_msalam mustafa_fadhil mustafa_fadhil ahmed_ghailani fazul_mohammed bin_laden bin_laden 6 abdullah_ahmed_abdullah ahmed_the_german fahid_msalam fahid_msalam swedan_sheikh abdullah_ahmed_abdullah swedan_sheikh swedan_sheikh 7 fazul_mohammed mohamed_owhali mohammed_odeh mohammed_odeh mustafa_fadhil abdal_rahmad mustafa_fadhil mustafa_fadhil 8 ahmed_ghailani bin_laden wadih_el-hage wadih_el-hage fahid_msalam ali_mohamed fahid_msalam fahid_msalam 9 ahmed_the_german wadih_el-hage bin_laden bin_laden mohammed_odeh khalid_al-fawwaz abdullah_ahmed_abdullah abdullah_ahmed_abdullah 10 ali_mohamed mohammed_odeh abdullah_ahmed_abdullah abdullah_ahmed_abdullah abdullah_ahmed_abdullah jihad_mohammed_ali mohammed_odeh mohammed_odeh