Network Level Measures
Measure Value Row count 37.000 Column count 37.000 Link count 33.000 Density 0.025 Components of 1 node (isolates) 11 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 3 Reciprocity 0.065 Characteristic path length 2.790 Clustering coefficient 0.049 Network levels (diameter) 6.000 Network fragmentation 0.730 Krackhardt connectedness 0.270 Krackhardt efficiency 0.949 Krackhardt hierarchy 0.865 Krackhardt upperboundedness 0.994 Degree centralization 0.045 Betweenness centralization 0.061 Closeness centralization 0.017 Eigenvector centralization 0.460 Reciprocal (symmetric)? No (6% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.056 0.013 0.014 Total degree centrality [Unscaled] 0.000 8.000 1.838 1.980 In-degree centrality 0.000 0.069 0.013 0.016 In-degree centrality [Unscaled] 0.000 5.000 0.919 1.124 Out-degree centrality 0.000 0.069 0.013 0.016 Out-degree centrality [Unscaled] 0.000 5.000 0.919 1.148 Eigenvector centrality 0.000 0.590 0.155 0.173 Eigenvector centrality [Unscaled] 0.000 0.417 0.109 0.123 Eigenvector centrality per component 0.000 0.214 0.066 0.058 Closeness centrality 0.014 0.025 0.016 0.004 Closeness centrality [Unscaled] 0.000 0.001 0.000 0.000 In-Closeness centrality 0.014 0.023 0.016 0.003 In-Closeness centrality [Unscaled] 0.000 0.001 0.000 0.000 Betweenness centrality 0.000 0.066 0.007 0.015 Betweenness centrality [Unscaled] 0.000 82.667 8.369 18.558 Hub centrality 0.000 1.069 0.072 0.221 Authority centrality 0.000 1.414 0.038 0.229 Information centrality 0.000 0.084 0.027 0.026 Information centrality [Unscaled] 0.000 1.684 0.542 0.523 Clique membership count 0.000 2.000 0.243 0.541 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 0.049 0.126
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: 37, density: 0.0247748)
Rank Task Value Unscaled Context* 1 bomb_preparation 0.056 8.000 1.205 2 bombing 0.056 8.000 1.205 3 get_money 0.042 6.000 0.661 4 driving 0.028 4.000 0.118 5 conceal_bomb_in_car 0.021 3.000 -0.154 6 explosion 0.021 3.000 -0.154 7 leave_bomb_and_car 0.021 3.000 -0.154 8 purchase_vehicle 0.021 3.000 -0.154 9 accusation 0.014 2.000 -0.426 10 convicted 0.014 2.000 -0.426 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.013 Mean in random network: 0.025 Std.dev: 0.014 Std.dev in random network: 0.026 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 get_money 0.069 5.000 2 bombing 0.056 4.000 3 bomb_preparation 0.042 3.000 4 conceal_bomb_in_car 0.028 2.000 5 driving_training 0.028 2.000 6 explosion 0.028 2.000 7 accusation 0.014 1.000 8 convicted 0.014 1.000 9 detonate_bomb 0.014 1.000 10 driving 0.014 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 bomb_preparation 0.069 5.000 2 bombing 0.056 4.000 3 driving 0.042 3.000 4 destruction 0.028 2.000 5 leave_bomb_and_car 0.028 2.000 6 murder 0.028 2.000 7 purchase_vehicle 0.028 2.000 8 accusation 0.014 1.000 9 arrest 0.014 1.000 10 conceal_bomb_in_car 0.014 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: 37, density: 0.0247748)
Rank Task Value Unscaled Context* 1 bombing 0.590 0.417 -1.204 2 bomb_preparation 0.534 0.377 -1.352 3 get_money 0.476 0.337 -1.504 4 purchase_vehicle 0.375 0.265 -1.771 5 driving 0.357 0.253 -1.820 6 conceal_bomb_in_car 0.332 0.235 -1.886 7 explosion 0.318 0.225 -1.923 8 purchase_acetylene 0.289 0.205 -1.999 9 purchase_oxygen 0.289 0.205 -1.999 10 driving_training 0.271 0.192 -2.047 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.155 Mean in random network: 1.044 Std.dev: 0.173 Std.dev in random network: 0.377 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 bombing 0.214 2 bomb_preparation 0.194 3 get_money 0.173 4 purchase_vehicle 0.136 5 driving 0.130 6 conceal_bomb_in_car 0.121 7 explosion 0.115 8 purchase_acetylene 0.105 9 purchase_oxygen 0.105 10 driving_training 0.098 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: 37, density: 0.0247748)
Rank Task Value Unscaled Context* 1 destruction 0.025 0.001 53.744 2 murder 0.025 0.001 53.744 3 explosion 0.024 0.001 53.549 4 bomb_preparation 0.023 0.001 53.408 5 bombing 0.023 0.001 53.408 6 driving 0.023 0.001 53.384 7 leave_bomb_and_car 0.023 0.001 53.384 8 conceal_bomb_in_car 0.023 0.001 53.382 9 weapon_training 0.022 0.001 53.375 10 detonate_bomb 0.022 0.001 53.359 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.016 Mean in random network: -0.302 Std.dev: 0.004 Std.dev in random network: 0.006 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 provide_money 0.023 0.001 2 get_money 0.023 0.001 3 rent_residence 0.019 0.001 4 driving_training 0.019 0.001 5 purchase_acetylene 0.018 0.001 6 purchase_oxygen 0.018 0.001 7 run_bomb_factory 0.018 0.001 8 surveillence 0.018 0.001 9 purchase_vehicle 0.018 0.001 10 bomb_preparation 0.018 0.000 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: 37, density: 0.0247748)
Rank Task Value Unscaled Context* 1 bomb_preparation 0.066 82.667 -0.119 2 bombing 0.056 71.000 -0.193 3 leave_bomb_and_car 0.027 34.000 -0.425 4 conceal_bomb_in_car 0.024 29.667 -0.452 5 detonate_bomb 0.022 28.000 -0.463 6 get_money 0.012 15.000 -0.545 7 driving 0.010 12.333 -0.561 8 run_bomb_factory 0.008 10.000 -0.576 9 purchase_acetylene 0.007 8.333 -0.587 10 purchase_oxygen 0.007 8.333 -0.587 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.007 Mean in random network: 0.081 Std.dev: 0.015 Std.dev in random network: 0.126 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 purchase_vehicle 1.069 2 purchase_acetylene 0.535 3 purchase_oxygen 0.535 4 rent_residence 0.535 5 bomb_preparation 0.000 6 destruction 0.000 7 murder 0.000 8 explosion 0.000 9 bombing 0.000 10 driving 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): Task x Task
Rank Task Value 1 get_money 1.414 2 bombing 0.000 3 explosion 0.000 4 purchase_acetylene 0.000 5 purchase_oxygen 0.000 6 run_bomb_factory 0.000 7 weapon_training 0.000 8 bomb_preparation 0.000 9 driving_training 0.000 10 detonate_bomb 0.000 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Task x Task
Rank Task Value Unscaled 1 bomb_preparation 0.084 1.684 2 bombing 0.079 1.587 3 driving 0.070 1.412 4 leave_bomb_and_car 0.061 1.221 5 destruction 0.057 1.139 6 murder 0.057 1.139 7 purchase_vehicle 0.057 1.136 8 get_money 0.042 0.850 9 conceal_bomb_in_car 0.042 0.836 10 detonate_bomb 0.040 0.800 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 bombing 2.000 2 explosion 2.000 3 conceal_bomb_in_car 1.000 4 destruction 1.000 5 driving 1.000 6 leave_bomb_and_car 1.000 7 murder 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 destruction 0.500 2 murder 0.500 3 explosion 0.333 4 conceal_bomb_in_car 0.167 5 leave_bomb_and_car 0.167 6 driving 0.083 7 bombing 0.048
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 bomb_preparation destruction bombing bombing get_money provide_money bomb_preparation bomb_preparation 2 bombing murder bomb_preparation bomb_preparation bombing get_money bombing bombing 3 leave_bomb_and_car explosion get_money get_money bomb_preparation rent_residence driving get_money 4 conceal_bomb_in_car bomb_preparation purchase_vehicle purchase_vehicle conceal_bomb_in_car driving_training destruction driving 5 detonate_bomb bombing driving driving driving_training purchase_acetylene leave_bomb_and_car conceal_bomb_in_car 6 get_money driving conceal_bomb_in_car conceal_bomb_in_car explosion purchase_oxygen murder explosion 7 driving leave_bomb_and_car explosion explosion accusation run_bomb_factory purchase_vehicle leave_bomb_and_car 8 run_bomb_factory conceal_bomb_in_car purchase_acetylene purchase_acetylene convicted surveillence accusation purchase_vehicle 9 purchase_acetylene weapon_training purchase_oxygen purchase_oxygen detonate_bomb purchase_vehicle arrest accusation 10 purchase_oxygen detonate_bomb driving_training driving_training driving bomb_preparation conceal_bomb_in_car convicted