Network Level Measures
Measure Value Row count 25.000 Column count 25.000 Link count 33.000 Density 0.055 Components of 1 node (isolates) 0 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.000 Characteristic path length 2.982 Clustering coefficient 0.116 Network levels (diameter) 6.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.967 Krackhardt hierarchy 1.000 Krackhardt upperboundedness 0.768 Degree centralization 0.048 Betweenness centralization 0.095 Closeness centralization 0.129 Eigenvector centralization 0.373 Reciprocal (symmetric)? No (0% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.010 0.073 0.028 0.015 Total degree centrality [Unscaled] 1.000 7.000 2.720 1.484 In-degree centrality 0.000 0.125 0.028 0.025 In-degree centrality [Unscaled] 0.000 6.000 1.360 1.196 Out-degree centrality 0.000 0.104 0.028 0.021 Out-degree centrality [Unscaled] 0.000 5.000 1.360 1.015 Eigenvector centrality 0.053 0.590 0.248 0.136 Eigenvector centrality [Unscaled] 0.037 0.418 0.175 0.096 Eigenvector centrality per component 0.037 0.418 0.175 0.096 Closeness centrality 0.020 0.091 0.030 0.014 Closeness centrality [Unscaled] 0.001 0.004 0.001 0.001 In-Closeness centrality 0.020 0.130 0.041 0.038 In-Closeness centrality [Unscaled] 0.001 0.005 0.002 0.002 Betweenness centrality 0.000 0.114 0.023 0.034 Betweenness centrality [Unscaled] 0.000 63.000 12.787 18.596 Hub centrality 0.000 1.414 0.057 0.277 Authority centrality 0.000 1.069 0.107 0.262 Information centrality 0.000 0.072 0.040 0.017 Information centrality [Unscaled] 0.000 1.264 0.701 0.305 Clique membership count 0.000 3.000 0.480 0.755 Simmelian ties 0.000 0.000 0.000 0.000 Simmelian ties [Unscaled] 0.000 0.000 0.000 0.000 Clustering coefficient 0.000 0.500 0.116 0.186
Key Nodes
This chart shows the Task that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Task 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: task x task (size: 25, density: 0.055)
Rank Task Value Unscaled Context* 1 overall_planning_and_execution 0.073 7.000 0.393 2 get_money 0.063 6.000 0.164 3 detonate 0.052 5.000 -0.064 4 bomb_preparation 0.042 4.000 -0.292 5 purchase_vehicle 0.042 4.000 -0.292 6 load_bomb 0.031 3.000 -0.521 7 brief_attack_team 0.031 3.000 -0.521 8 lead_attackers_to_embassy 0.031 3.000 -0.521 9 driving 0.031 3.000 -0.521 10 leave_bomb_and_car 0.031 3.000 -0.521 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.028 Mean in random network: 0.055 Std.dev: 0.015 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): task x task
Rank Task Value Unscaled 1 overall_planning_and_execution 0.125 6.000 2 bomb_preparation 0.063 3.000 3 brief_attack_team 0.042 2.000 4 lead_attackers_to_embassy 0.042 2.000 5 driving 0.042 2.000 6 leave_bomb_and_car 0.042 2.000 7 purchase_vehicle 0.042 2.000 8 detonate 0.042 2.000 9 load_bomb 0.021 1.000 10 review_surveillance_files 0.021 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): task x task
Rank Task Value Unscaled 1 get_money 0.104 5.000 2 detonate 0.063 3.000 3 load_bomb 0.042 2.000 4 final_reconnaissance_mission 0.042 2.000 5 purchase_vehicle 0.042 2.000 6 surveillance_of_possible_targets 0.042 2.000 7 education_and_training 0.042 2.000 8 provide_money 0.042 2.000 9 overall_planning_and_execution 0.021 1.000 10 review_surveillance_files 0.021 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: task x task (size: 25, density: 0.055)
Rank Task Value Unscaled Context* 1 get_money 0.590 0.418 1.056 2 overall_planning_and_execution 0.564 0.399 0.970 3 purchase_vehicle 0.498 0.352 0.758 4 surveillance_of_possible_targets 0.328 0.232 0.212 5 brief_attack_team 0.325 0.230 0.203 6 lead_attackers_to_embassy 0.276 0.195 0.044 7 provide_money 0.274 0.193 0.036 8 review_surveillance_files 0.266 0.188 0.010 9 final_reconnaissance_mission 0.265 0.187 0.007 10 education_and_training 0.265 0.187 0.007 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.248 Mean in random network: 0.262 Std.dev: 0.136 Std.dev in random network: 0.311 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): task x task
Rank Task Value 1 get_money 0.418 2 overall_planning_and_execution 0.399 3 purchase_vehicle 0.352 4 surveillance_of_possible_targets 0.232 5 brief_attack_team 0.230 6 lead_attackers_to_embassy 0.195 7 provide_money 0.193 8 review_surveillance_files 0.188 9 final_reconnaissance_mission 0.187 10 education_and_training 0.187 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: task x task (size: 25, density: 0.055)
Rank Task Value Unscaled Context* 1 provide_money 0.091 0.004 -2.132 2 get_money 0.053 0.002 -3.805 3 rent_residence 0.032 0.001 -4.723 4 run_bomb_factory 0.031 0.001 -4.797 5 purchase_oxygen 0.031 0.001 -4.797 6 purchase_acetylene 0.031 0.001 -4.797 7 bomb_preparation 0.029 0.001 -4.865 8 load_bomb 0.028 0.001 -4.928 9 final_reconnaissance_mission 0.028 0.001 -4.934 10 purchase_vehicle 0.028 0.001 -4.934 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.030 Mean in random network: 0.138 Std.dev: 0.014 Std.dev in random network: 0.022 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): task x task
Rank Task Value Unscaled 1 clean_of_evidence 0.130 0.005 2 film_videotape_announcing_martyrdom 0.130 0.005 3 explosion 0.130 0.005 4 detonate 0.113 0.005 5 lead_attackers_to_embassy 0.049 0.002 6 leave_bomb_and_car 0.038 0.002 7 conceal_bomb_in_car 0.029 0.001 8 overall_planning_and_execution 0.028 0.001 9 load_bomb 0.028 0.001 10 bomb_preparation 0.026 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: task x task (size: 25, density: 0.055)
Rank Task Value Unscaled Context* 1 detonate 0.114 63.000 0.057 2 lead_attackers_to_embassy 0.106 58.667 0.030 3 load_bomb 0.074 40.667 -0.084 4 bomb_preparation 0.072 39.667 -0.090 5 overall_planning_and_execution 0.063 35.000 -0.120 6 leave_bomb_and_car 0.031 17.333 -0.231 7 driving 0.024 13.333 -0.256 8 get_money 0.020 11.000 -0.271 9 run_bomb_factory 0.016 9.000 -0.284 10 purchase_vehicle 0.013 7.333 -0.294 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.023 Mean in random network: 0.098 Std.dev: 0.034 Std.dev in random network: 0.287 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): task x task
Rank Task Value 1 get_money 1.414 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): task x task
Rank Task Value 1 purchase_vehicle 1.069 2 purchase_oxygen 0.535 3 purchase_acetylene 0.535 4 rent_residence 0.535 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): task x task
Rank Task Value Unscaled 1 get_money 0.072 1.264 2 detonate 0.063 1.101 3 load_bomb 0.057 1.005 4 purchase_vehicle 0.053 0.933 5 surveillance_of_possible_targets 0.053 0.931 6 provide_money 0.053 0.927 7 education_and_training 0.053 0.921 8 final_reconnaissance_mission 0.053 0.921 9 bomb_preparation 0.043 0.752 10 overall_planning_and_execution 0.042 0.735 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): task x task
Rank Task Value 1 overall_planning_and_execution 3.000 2 brief_attack_team 2.000 3 review_surveillance_files 1.000 4 final_reconnaissance_mission 1.000 5 driving_training 1.000 6 driving 1.000 7 purchase_vehicle 1.000 8 surveillance_of_possible_targets 1.000 9 education_and_training 1.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): task x task
Rank Task Value Unscaled 1 All nodes have this value 0.000 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): task x task
Rank Task Value 1 review_surveillance_files 0.500 2 final_reconnaissance_mission 0.500 3 driving_training 0.500 4 education_and_training 0.500 5 brief_attack_team 0.333 6 driving 0.167 7 purchase_vehicle 0.167 8 surveillance_of_possible_targets 0.167 9 overall_planning_and_execution 0.071
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 detonate provide_money get_money get_money overall_planning_and_execution clean_of_evidence get_money overall_planning_and_execution 2 lead_attackers_to_embassy get_money overall_planning_and_execution overall_planning_and_execution bomb_preparation film_videotape_announcing_martyrdom detonate get_money 3 load_bomb rent_residence purchase_vehicle purchase_vehicle brief_attack_team explosion load_bomb detonate 4 bomb_preparation run_bomb_factory surveillance_of_possible_targets surveillance_of_possible_targets lead_attackers_to_embassy detonate final_reconnaissance_mission bomb_preparation 5 overall_planning_and_execution purchase_oxygen brief_attack_team brief_attack_team driving lead_attackers_to_embassy purchase_vehicle purchase_vehicle 6 leave_bomb_and_car purchase_acetylene lead_attackers_to_embassy lead_attackers_to_embassy leave_bomb_and_car leave_bomb_and_car surveillance_of_possible_targets load_bomb 7 driving bomb_preparation provide_money provide_money purchase_vehicle conceal_bomb_in_car education_and_training brief_attack_team 8 get_money load_bomb review_surveillance_files review_surveillance_files detonate overall_planning_and_execution provide_money lead_attackers_to_embassy 9 run_bomb_factory final_reconnaissance_mission final_reconnaissance_mission final_reconnaissance_mission load_bomb load_bomb overall_planning_and_execution driving 10 purchase_vehicle purchase_vehicle education_and_training education_and_training review_surveillance_files bomb_preparation review_surveillance_files leave_bomb_and_car