Network Level Measures
Measure Value Row count 21.000 Column count 21.000 Link count 20.000 Density 0.048 Components of 1 node (isolates) 10 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 3 Reciprocity 0.818 Characteristic path length 1.231 Clustering coefficient 0.167 Network levels (diameter) 2.000 Network fragmentation 0.924 Krackhardt connectedness 0.076 Krackhardt efficiency 0.625 Krackhardt hierarchy 0.375 Krackhardt upperboundedness 1.000 Degree centralization 0.141 Betweenness centralization 0.008 Closeness centralization 0.017 Eigenvector centralization 0.659 Reciprocal (symmetric)? No (81% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.175 0.048 0.060 Total degree centrality [Unscaled] 0.000 7.000 1.905 2.389 In-degree centrality 0.000 0.150 0.048 0.059 In-degree centrality [Unscaled] 0.000 3.000 0.952 1.174 Out-degree centrality 0.000 0.200 0.048 0.063 Out-degree centrality [Unscaled] 0.000 4.000 0.952 1.253 Eigenvector centrality 0.000 0.740 0.144 0.273 Eigenvector centrality [Unscaled] 0.000 0.524 0.102 0.193 Eigenvector centrality per component 0.000 0.125 0.047 0.049 Closeness centrality 0.048 0.059 0.051 0.004 Closeness centrality [Unscaled] 0.002 0.003 0.003 0.000 In-Closeness centrality 0.048 0.058 0.051 0.004 In-Closeness centrality [Unscaled] 0.002 0.003 0.003 0.000 Betweenness centrality 0.000 0.008 0.001 0.002 Betweenness centrality [Unscaled] 0.000 3.000 0.286 0.765 Hub centrality 0.000 0.776 0.134 0.278 Authority centrality 0.000 0.703 0.145 0.273 Information centrality -0.000 0.167 0.048 0.068 Information centrality [Unscaled] -0.000 0.000 -0.000 0.000 Clique membership count 0.000 1.000 0.190 0.393 Simmelian ties 0.000 0.150 0.029 0.059 Simmelian ties [Unscaled] 0.000 3.000 0.571 1.178 Clustering coefficient 0.000 1.000 0.167 0.356
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: 21, density: 0.047619)
Rank Agent Value Unscaled Context* 1 ahmed_ghailani 0.175 7.000 2.741 2 swedan_sheikh 0.150 6.000 2.203 3 mustafa_fadhil 0.150 6.000 2.203 4 fahid_msalam 0.150 6.000 2.203 5 bin_laden 0.100 4.000 1.127 6 abdullah_ahmed_abdullah 0.075 3.000 0.589 7 ali_mohamed 0.050 2.000 0.051 8 khalid_al-fawwaz 0.050 2.000 0.051 9 abdal_rahmad 0.050 2.000 0.051 10 ahmed_the_german 0.025 1.000 -0.487 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.048 Mean in random network: 0.048 Std.dev: 0.060 Std.dev in random network: 0.046 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 ahmed_ghailani 0.150 3.000 2 swedan_sheikh 0.150 3.000 3 mustafa_fadhil 0.150 3.000 4 fahid_msalam 0.150 3.000 5 bin_laden 0.100 2.000 6 abdullah_ahmed_abdullah 0.100 2.000 7 fazul_mohammed 0.050 1.000 8 ali_mohamed 0.050 1.000 9 khalid_al-fawwaz 0.050 1.000 10 abdal_rahmad 0.050 1.000 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 ahmed_ghailani 0.200 4.000 2 swedan_sheikh 0.150 3.000 3 mustafa_fadhil 0.150 3.000 4 fahid_msalam 0.150 3.000 5 bin_laden 0.100 2.000 6 ahmed_the_german 0.050 1.000 7 ali_mohamed 0.050 1.000 8 khalid_al-fawwaz 0.050 1.000 9 abdullah_ahmed_abdullah 0.050 1.000 10 abdal_rahmad 0.050 1.000 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: 21, density: 0.047619)
Rank Agent Value Unscaled Context* 1 ahmed_ghailani 0.740 0.524 1.293 2 swedan_sheikh 0.682 0.482 1.124 3 mustafa_fadhil 0.682 0.482 1.124 4 fahid_msalam 0.682 0.482 1.124 5 fazul_mohammed 0.240 0.170 -0.153 6 ahmed_the_german 0.000 0.000 -0.846 7 bin_laden 0.000 0.000 -0.846 8 ali_mohamed 0.000 0.000 -0.846 9 khalid_al-fawwaz 0.000 0.000 -0.846 10 abdullah_ahmed_abdullah 0.000 0.000 -0.846 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.144 Mean in random network: 0.293 Std.dev: 0.273 Std.dev in random network: 0.346 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 ahmed_ghailani 0.125 2 swedan_sheikh 0.115 3 mustafa_fadhil 0.115 4 fahid_msalam 0.115 5 bin_laden 0.101 6 abdullah_ahmed_abdullah 0.101 7 ahmed_the_german 0.071 8 ali_mohamed 0.071 9 khalid_al-fawwaz 0.071 10 abdal_rahmad 0.071 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: 21, density: 0.047619)
Rank Agent Value Unscaled Context* 1 ahmed_ghailani 0.059 0.003 0.793 2 swedan_sheikh 0.059 0.003 0.781 3 mustafa_fadhil 0.059 0.003 0.781 4 fahid_msalam 0.059 0.003 0.781 5 bin_laden 0.053 0.003 0.356 6 ahmed_the_german 0.052 0.003 0.347 7 ali_mohamed 0.052 0.003 0.347 8 khalid_al-fawwaz 0.052 0.003 0.347 9 abdullah_ahmed_abdullah 0.050 0.002 0.171 10 abdal_rahmad 0.050 0.002 0.171 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.051 Mean in random network: 0.048 Std.dev: 0.004 Std.dev in random network: 0.014 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_mohammed 0.058 0.003 2 ahmed_ghailani 0.056 0.003 3 swedan_sheikh 0.056 0.003 4 mustafa_fadhil 0.056 0.003 5 fahid_msalam 0.056 0.003 6 bin_laden 0.053 0.003 7 abdullah_ahmed_abdullah 0.053 0.003 8 ali_mohamed 0.052 0.003 9 khalid_al-fawwaz 0.052 0.003 10 abdal_rahmad 0.052 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: 21, density: 0.047619)
Rank Agent Value Unscaled Context* 1 ahmed_ghailani 0.008 3.000 -0.366 2 bin_laden 0.005 2.000 -0.374 3 abdullah_ahmed_abdullah 0.003 1.000 -0.383 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.001 Mean in random network: 0.122 Std.dev: 0.002 Std.dev in random network: 0.312 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 ahmed_ghailani 0.776 2 swedan_sheikh 0.682 3 mustafa_fadhil 0.682 4 fahid_msalam 0.682 5 ahmed_the_german 0.000 6 bin_laden 0.000 7 ali_mohamed 0.000 8 khalid_al-fawwaz 0.000 9 abdal_rahmad 0.000 10 abdullah_ahmed_abdullah 0.000 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 0.703 2 mustafa_fadhil 0.703 3 fahid_msalam 0.703 4 ahmed_ghailani 0.672 5 fazul_mohammed 0.255 6 bin_laden 0.000 7 ali_mohamed 0.000 8 khalid_al-fawwaz 0.000 9 abdullah_ahmed_abdullah 0.000 10 abdal_rahmad 0.000 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Agent x Agent
Rank Agent Value 1 swedan_sheikh 0.167 2 mustafa_fadhil 0.167 3 fahid_msalam 0.167 4 ahmed_ghailani 0.167 5 ahmed_the_german 0.167 6 khalid_al-fawwaz 0.037 7 bin_laden 0.037 8 ali_mohamed 0.037 9 abdal_rahmad 0.027 10 abdullah_ahmed_abdullah 0.027 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 ahmed_ghailani 1.000 2 swedan_sheikh 1.000 3 mustafa_fadhil 1.000 4 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 0.150 3.000 2 swedan_sheikh 0.150 3.000 3 mustafa_fadhil 0.150 3.000 4 fahid_msalam 0.150 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 swedan_sheikh 1.000 2 mustafa_fadhil 1.000 3 fahid_msalam 1.000 4 ahmed_ghailani 0.500
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 ahmed_ghailani ahmed_ghailani ahmed_ghailani ahmed_ghailani ahmed_ghailani fazul_mohammed ahmed_ghailani ahmed_ghailani 2 bin_laden swedan_sheikh swedan_sheikh swedan_sheikh swedan_sheikh ahmed_ghailani swedan_sheikh swedan_sheikh 3 abdullah_ahmed_abdullah mustafa_fadhil mustafa_fadhil mustafa_fadhil mustafa_fadhil swedan_sheikh mustafa_fadhil mustafa_fadhil 4 ahmed_the_german fahid_msalam fahid_msalam fahid_msalam fahid_msalam mustafa_fadhil fahid_msalam fahid_msalam 5 fazul_mohammed bin_laden fazul_mohammed bin_laden bin_laden fahid_msalam bin_laden bin_laden 6 ali_mohamed ahmed_the_german ahmed_the_german abdullah_ahmed_abdullah abdullah_ahmed_abdullah bin_laden ahmed_the_german abdullah_ahmed_abdullah 7 mohammed_salim ali_mohamed bin_laden ahmed_the_german fazul_mohammed abdullah_ahmed_abdullah ali_mohamed ali_mohamed 8 jamal_al-fadl khalid_al-fawwaz ali_mohamed ali_mohamed ali_mohamed ali_mohamed khalid_al-fawwaz khalid_al-fawwaz 9 khalid_al-fawwaz abdullah_ahmed_abdullah khalid_al-fawwaz khalid_al-fawwaz khalid_al-fawwaz khalid_al-fawwaz abdullah_ahmed_abdullah abdal_rahmad 10 jihad_mohammed_ali abdal_rahmad abdullah_ahmed_abdullah abdal_rahmad abdal_rahmad abdal_rahmad abdal_rahmad ahmed_the_german