Network Level Measures
Measure Value Row count 18.000 Column count 18.000 Link count 18.000 Density 0.059 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 3.649 Clustering coefficient 0.000 Network levels (diameter) 8.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.993 Krackhardt hierarchy 1.000 Krackhardt upperboundedness 0.963 Degree centralization 0.066 Betweenness centralization 0.170 Closeness centralization 0.130 Eigenvector centralization 0.397 Reciprocal (symmetric)? No (0% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.029 0.118 0.059 0.026 Total degree centrality [Unscaled] 1.000 4.000 2.000 0.882 In-degree centrality 0.000 0.176 0.059 0.039 In-degree centrality [Unscaled] 0.000 3.000 1.000 0.667 Out-degree centrality 0.000 0.118 0.059 0.034 Out-degree centrality [Unscaled] 0.000 2.000 1.000 0.577 Eigenvector centrality 0.023 0.625 0.272 0.193 Eigenvector centrality [Unscaled] 0.016 0.442 0.192 0.136 Eigenvector centrality per component 0.016 0.442 0.192 0.136 Closeness centrality 0.056 0.150 0.091 0.038 Closeness centrality [Unscaled] 0.003 0.009 0.005 0.002 In-Closeness centrality 0.056 0.110 0.082 0.017 In-Closeness centrality [Unscaled] 0.003 0.006 0.005 0.001 Betweenness centrality 0.000 0.221 0.060 0.065 Betweenness centrality [Unscaled] 0.000 60.000 16.333 17.739 Hub centrality 0.000 0.816 0.136 0.304 Authority centrality 0.000 1.414 0.079 0.324 Information centrality 0.000 0.096 0.056 0.027 Information centrality [Unscaled] 0.000 1.135 0.654 0.319 Clique membership count 0.000 0.000 0.000 0.000 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 0.000 0.000 0.000
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: 18, density: 0.0588235)
Rank Task Value Unscaled Context* 1 present_plan_to_sgc 0.118 4.000 1.061 2 infiltrate_summit 0.088 3.000 0.530 3 revenna_briefing 0.088 3.000 0.530 4 defend_revenna 0.088 3.000 0.530 5 find_tunnel_crystals 0.088 3.000 0.530 6 fly_ship_to_yuWorld 0.059 2.000 0.000 7 replace_jerran 0.059 2.000 0.000 8 create_memory_drug 0.059 2.000 0.000 9 fly_ship_to_summit 0.059 2.000 0.000 10 find_ring_room 0.059 2.000 0.000 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.059 Mean in random network: 0.059 Std.dev: 0.026 Std.dev in random network: 0.055 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 present_plan_to_sgc 0.176 3.000 2 find_tunnel_crystals 0.118 2.000 3 poison_summit 0.059 1.000 4 infiltrate_summit 0.059 1.000 5 fly_ship_to_yuWorld 0.059 1.000 6 poison_jaffa_at_gate 0.059 1.000 7 replace_jerran 0.059 1.000 8 create_memory_drug 0.059 1.000 9 spy_on_summit 0.059 1.000 10 fly_ship_to_summit 0.059 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 infiltrate_summit 0.118 2.000 2 revenna_briefing 0.118 2.000 3 defend_revenna 0.118 2.000 4 fly_ship_to_yuWorld 0.059 1.000 5 create_poison 0.059 1.000 6 replace_jerran 0.059 1.000 7 create_memory_drug 0.059 1.000 8 present_plan_to_sgc 0.059 1.000 9 inflitrate_yuWorld 0.059 1.000 10 fly_ship_to_summit 0.059 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: 18, density: 0.0588235)
Rank Task Value Unscaled Context* 1 defend_revenna 0.625 0.442 1.178 2 revenna_briefing 0.602 0.426 1.109 3 present_plan_to_sgc 0.531 0.376 0.899 4 find_tunnel_crystals 0.514 0.364 0.849 5 hide_data_crystal 0.411 0.291 0.541 6 retrieve_data_crystal 0.379 0.268 0.445 7 fly_ship_to_yuWorld 0.314 0.222 0.253 8 create_memory_drug 0.261 0.185 0.095 9 find_ring_room 0.253 0.179 0.071 10 inflitrate_yuWorld 0.217 0.154 -0.035 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.272 Mean in random network: 0.229 Std.dev: 0.193 Std.dev in random network: 0.336 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 defend_revenna 0.442 2 revenna_briefing 0.426 3 present_plan_to_sgc 0.376 4 find_tunnel_crystals 0.364 5 hide_data_crystal 0.291 6 retrieve_data_crystal 0.268 7 fly_ship_to_yuWorld 0.222 8 create_memory_drug 0.185 9 find_ring_room 0.179 10 inflitrate_yuWorld 0.154 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: 18, density: 0.0588235)
Rank Task Value Unscaled Context* 1 get_reole_chemical 0.150 0.009 1.642 2 create_poison 0.147 0.009 1.442 3 create_memory_drug 0.147 0.009 1.442 4 inflitrate_yuWorld 0.147 0.009 1.442 5 present_plan_to_sgc 0.142 0.008 1.190 6 revenna_briefing 0.136 0.008 0.898 7 defend_revenna 0.076 0.004 -2.217 8 fly_ship_to_yuWorld 0.074 0.004 -2.302 9 replace_jerran 0.070 0.004 -2.505 10 hide_data_crystal 0.070 0.004 -2.520 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.091 Mean in random network: 0.119 Std.dev: 0.038 Std.dev in random network: 0.019 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 poison_jaffa_at_gate 0.110 0.006 2 find_ring_room 0.105 0.006 3 find_tunnel_crystals 0.100 0.006 4 poison_summit 0.097 0.006 5 spy_on_summit 0.097 0.006 6 infiltrate_summit 0.092 0.005 7 fly_ship_to_summit 0.088 0.005 8 retrieve_data_crystal 0.088 0.005 9 replace_jerran 0.084 0.005 10 hide_data_crystal 0.084 0.005 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: 18, density: 0.0588235)
Rank Task Value Unscaled Context* 1 revenna_briefing 0.221 60.000 0.297 2 present_plan_to_sgc 0.191 52.000 0.196 3 fly_ship_to_yuWorld 0.110 30.000 -0.083 4 defend_revenna 0.110 30.000 -0.083 5 replace_jerran 0.103 28.000 -0.109 6 fly_ship_to_summit 0.088 24.000 -0.159 7 infiltrate_summit 0.066 18.000 -0.235 8 find_tunnel_crystals 0.066 18.000 -0.235 9 create_memory_drug 0.051 14.000 -0.286 10 find_ring_room 0.037 10.000 -0.337 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.060 Mean in random network: 0.134 Std.dev: 0.065 Std.dev in random network: 0.290 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 create_poison 0.816 2 create_memory_drug 0.816 3 inflitrate_yuWorld 0.816 4 defend_revenna 0.000 5 retrieve_data_crystal 0.000 6 infiltrate_summit 0.000 7 revenna_briefing 0.000 8 fly_ship_to_yuWorld 0.000 9 replace_jerran 0.000 10 present_plan_to_sgc 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 present_plan_to_sgc 1.414 2 find_tunnel_crystals 0.000 3 hide_data_crystal 0.000 4 poison_summit 0.000 5 fly_ship_to_yuWorld 0.000 6 spy_on_summit 0.000 7 defend_revenna 0.000 8 infiltrate_summit 0.000 9 poison_jaffa_at_gate 0.000 10 replace_jerran 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 revenna_briefing 0.096 1.135 2 defend_revenna 0.088 1.031 3 infiltrate_summit 0.080 0.941 4 present_plan_to_sgc 0.074 0.869 5 fly_ship_to_yuWorld 0.063 0.743 6 find_tunnel_crystals 0.063 0.743 7 replace_jerran 0.062 0.729 8 create_memory_drug 0.061 0.724 9 hide_data_crystal 0.061 0.724 10 retrieve_data_crystal 0.061 0.724 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 All nodes have this value 0.000 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 All nodes have this value 0.000
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 revenna_briefing get_reole_chemical defend_revenna defend_revenna present_plan_to_sgc poison_jaffa_at_gate infiltrate_summit present_plan_to_sgc 2 present_plan_to_sgc create_poison revenna_briefing revenna_briefing find_tunnel_crystals find_ring_room revenna_briefing infiltrate_summit 3 fly_ship_to_yuWorld create_memory_drug present_plan_to_sgc present_plan_to_sgc poison_summit find_tunnel_crystals defend_revenna revenna_briefing 4 defend_revenna inflitrate_yuWorld find_tunnel_crystals find_tunnel_crystals infiltrate_summit poison_summit fly_ship_to_yuWorld defend_revenna 5 replace_jerran present_plan_to_sgc hide_data_crystal hide_data_crystal fly_ship_to_yuWorld spy_on_summit create_poison find_tunnel_crystals 6 fly_ship_to_summit revenna_briefing retrieve_data_crystal retrieve_data_crystal poison_jaffa_at_gate infiltrate_summit replace_jerran fly_ship_to_yuWorld 7 infiltrate_summit defend_revenna fly_ship_to_yuWorld fly_ship_to_yuWorld replace_jerran fly_ship_to_summit create_memory_drug replace_jerran 8 find_tunnel_crystals fly_ship_to_yuWorld create_memory_drug create_memory_drug create_memory_drug retrieve_data_crystal present_plan_to_sgc create_memory_drug 9 create_memory_drug replace_jerran find_ring_room find_ring_room spy_on_summit replace_jerran inflitrate_yuWorld fly_ship_to_summit 10 find_ring_room hide_data_crystal inflitrate_yuWorld inflitrate_yuWorld fly_ship_to_summit hide_data_crystal fly_ship_to_summit find_ring_room