Network Level Measures
Measure Value Row count 24.000 Column count 24.000 Link count 369.000 Density 0.668 Components of 1 node (isolates) 0 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.854 Characteristic path length 1.332 Clustering coefficient 0.788 Network levels (diameter) 2.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.304 Krackhardt hierarchy 0.000 Krackhardt upperboundedness 1.000 Degree centralization 0.362 Betweenness centralization 0.046 Closeness centralization 0.476 Eigenvector centralization 0.090 Reciprocal (symmetric)? No (85% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.261 1.000 0.668 0.211 Total degree centrality [Unscaled] 12.000 46.000 30.750 9.705 In-degree centrality 0.348 1.000 0.668 0.183 In-degree centrality [Unscaled] 8.000 23.000 15.375 4.211 Out-degree centrality 0.174 1.000 0.668 0.252 Out-degree centrality [Unscaled] 4.000 23.000 15.375 5.794 Eigenvector centrality 0.153 0.364 0.282 0.063 Eigenvector centrality [Unscaled] 0.108 0.258 0.199 0.045 Eigenvector centrality per component 0.108 0.258 0.199 0.045 Closeness centrality 0.548 1.000 0.777 0.139 Closeness centrality [Unscaled] 0.024 0.043 0.034 0.006 In-Closeness centrality 0.605 1.000 0.766 0.109 In-Closeness centrality [Unscaled] 0.026 0.043 0.033 0.005 Betweenness centrality 0.000 0.059 0.015 0.018 Betweenness centrality [Unscaled] 0.127 29.987 7.625 9.014 Hub centrality 0.080 0.382 0.273 0.093 Authority centrality 0.160 0.368 0.282 0.060 Information centrality 0.020 0.051 0.042 0.009 Information centrality [Unscaled] 3.245 8.529 6.924 1.524 Clique membership count 3.000 54.000 22.458 16.096 Simmelian ties 0.174 1.000 0.616 0.236 Simmelian ties [Unscaled] 4.000 23.000 14.167 5.429 Clustering coefficient 0.628 0.968 0.788 0.101
Key Nodes
This chart shows the Location that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Location 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: DIPLOMATIC_EXCHANGE (size: 24, density: 0.668478)
Rank Location Value Unscaled Context* 1 JAPANLSIAVAKIA 1.000 46.000 3.450 2 UNITED_STATESM 1.000 46.000 3.450 3 UNITED_KINGDOM 0.935 43.000 2.771 4 CHINALINAATESM 0.891 41.000 2.319 5 SWITZERLANDKIA 0.848 39.000 1.866 6 BRAZILINAATESM 0.826 38.000 1.640 7 SPAINTANANDKIA 0.826 38.000 1.640 8 EGYPTORLOVAKIA 0.804 37.000 1.414 9 YUGOSLAVIATESM 0.804 37.000 1.414 10 ARGENTINAATESM 0.783 36.000 1.188 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.668 Mean in random network: 0.668 Std.dev: 0.211 Std.dev in random network: 0.096 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): DIPLOMATIC_EXCHANGE
Rank Location Value Unscaled 1 JAPANLSIAVAKIA 1.000 23.000 2 UNITED_STATESM 1.000 23.000 3 UNITED_KINGDOM 0.913 21.000 4 CHINALINAATESM 0.870 20.000 5 BRAZILINAATESM 0.826 19.000 6 EGYPTORLOVAKIA 0.783 18.000 7 SPAINTANANDKIA 0.783 18.000 8 YUGOSLAVIATESM 0.783 18.000 9 ARGENTINAATESM 0.739 17.000 10 SWITZERLANDKIA 0.739 17.000 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): DIPLOMATIC_EXCHANGE
Rank Location Value Unscaled 1 JAPANLSIAVAKIA 1.000 23.000 2 UNITED_STATESM 1.000 23.000 3 SWITZERLANDKIA 0.957 22.000 4 UNITED_KINGDOM 0.957 22.000 5 CHINALINAATESM 0.913 21.000 6 SPAINTANANDKIA 0.870 20.000 7 ARGENTINAATESM 0.826 19.000 8 BRAZILINAATESM 0.826 19.000 9 EGYPTORLOVAKIA 0.826 19.000 10 YUGOSLAVIATESM 0.826 19.000 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: DIPLOMATIC_EXCHANGE (size: 24, density: 0.668478)
Rank Location Value Unscaled Context* 1 UNITED_STATESM 0.364 0.258 -1.591 2 JAPANLSIAVAKIA 0.364 0.258 -1.591 3 SWITZERLANDKIA 0.356 0.252 -1.623 4 UNITED_KINGDOM 0.347 0.246 -1.654 5 CHINALINAATESM 0.337 0.238 -1.693 6 SPAINTANANDKIA 0.334 0.236 -1.703 7 EGYPTORLOVAKIA 0.327 0.231 -1.729 8 YUGOSLAVIATESM 0.326 0.231 -1.732 9 BRAZILINAATESM 0.322 0.228 -1.748 10 ARGENTINAATESM 0.317 0.224 -1.766 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.282 Mean in random network: 0.795 Std.dev: 0.063 Std.dev in random network: 0.271 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): DIPLOMATIC_EXCHANGE
Rank Location Value 1 UNITED_STATESM 0.258 2 JAPANLSIAVAKIA 0.258 3 SWITZERLANDKIA 0.252 4 UNITED_KINGDOM 0.246 5 CHINALINAATESM 0.238 6 SPAINTANANDKIA 0.236 7 EGYPTORLOVAKIA 0.231 8 YUGOSLAVIATESM 0.231 9 BRAZILINAATESM 0.228 10 ARGENTINAATESM 0.224 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: DIPLOMATIC_EXCHANGE (size: 24, density: 0.668478)
Rank Location Value Unscaled Context* 1 JAPANLSIAVAKIA 1.000 0.043 6.634 2 UNITED_STATESM 1.000 0.043 6.634 3 SWITZERLANDKIA 0.958 0.042 5.600 4 UNITED_KINGDOM 0.958 0.042 5.600 5 CHINALINAATESM 0.920 0.040 4.648 6 SPAINTANANDKIA 0.885 0.038 3.770 7 ARGENTINAATESM 0.852 0.037 2.957 8 BRAZILINAATESM 0.852 0.037 2.957 9 EGYPTORLOVAKIA 0.852 0.037 2.957 10 YUGOSLAVIATESM 0.852 0.037 2.957 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.777 Mean in random network: 0.733 Std.dev: 0.139 Std.dev in random network: 0.040 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): DIPLOMATIC_EXCHANGE
Rank Location Value Unscaled 1 JAPANLSIAVAKIA 1.000 0.043 2 UNITED_STATESM 1.000 0.043 3 UNITED_KINGDOM 0.920 0.040 4 CHINALINAATESM 0.885 0.038 5 BRAZILINAATESM 0.852 0.037 6 EGYPTORLOVAKIA 0.821 0.036 7 SPAINTANANDKIA 0.821 0.036 8 YUGOSLAVIATESM 0.821 0.036 9 ARGENTINAATESM 0.793 0.034 10 SWITZERLANDKIA 0.793 0.034 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: DIPLOMATIC_EXCHANGE (size: 24, density: 0.668478)
Rank Location Value Unscaled Context* 1 JAPANLSIAVAKIA 0.059 29.987 7.411 2 UNITED_STATESM 0.059 29.987 7.411 3 UNITED_KINGDOM 0.046 23.198 5.291 4 CHINALINAATESM 0.042 21.373 4.722 5 EGYPTORLOVAKIA 0.021 10.548 1.343 6 BRAZILINAATESM 0.017 8.697 0.766 7 ARGENTINAATESM 0.017 8.563 0.724 8 SWITZERLANDKIA 0.017 8.479 0.698 9 SPAINTANANDKIA 0.016 8.300 0.642 10 YUGOSLAVIATESM 0.013 6.730 0.152 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.015 Mean in random network: 0.012 Std.dev: 0.018 Std.dev in random network: 0.006 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): DIPLOMATIC_EXCHANGE
Rank Location Value 1 JAPANLSIAVAKIA 0.382 2 UNITED_STATESM 0.382 3 SWITZERLANDKIA 0.374 4 UNITED_KINGDOM 0.365 5 CHINALINAATESM 0.357 6 SPAINTANANDKIA 0.352 7 YUGOSLAVIATESM 0.343 8 BRAZILINAATESM 0.333 9 EGYPTORLOVAKIA 0.332 10 ARGENTINAATESM 0.330 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): DIPLOMATIC_EXCHANGE
Rank Location Value 1 JAPANLSIAVAKIA 0.368 2 UNITED_STATESM 0.368 3 UNITED_KINGDOM 0.345 4 BRAZILINAATESM 0.344 5 YUGOSLAVIATESM 0.330 6 SPAINTANANDKIA 0.330 7 CHINALINAATESM 0.329 8 EGYPTORLOVAKIA 0.320 9 SWITZERLANDKIA 0.320 10 ARGENTINAATESM 0.309 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): DIPLOMATIC_EXCHANGE
Rank Location Value Unscaled 1 JAPANLSIAVAKIA 0.051 8.529 2 UNITED_STATESM 0.051 8.529 3 UNITED_KINGDOM 0.051 8.394 4 SWITZERLANDKIA 0.050 8.389 5 CHINALINAATESM 0.050 8.245 6 SPAINTANANDKIA 0.049 8.066 7 EGYPTORLOVAKIA 0.048 7.932 8 ARGENTINAATESM 0.047 7.892 9 YUGOSLAVIATESM 0.047 7.891 10 BRAZILINAATESM 0.047 7.875 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): DIPLOMATIC_EXCHANGE
Rank Location Value 1 JAPANLSIAVAKIA 54.000 2 UNITED_STATESM 54.000 3 SWITZERLANDKIA 50.000 4 SPAINTANANDKIA 38.000 5 YUGOSLAVIATESM 38.000 6 EGYPTORLOVAKIA 35.000 7 BRAZILINAATESM 34.000 8 UNITED_KINGDOM 33.000 9 CZECHOSLOVAKIA 28.000 10 CHINALINAATESM 26.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): DIPLOMATIC_EXCHANGE
Rank Location Value Unscaled 1 JAPANLSIAVAKIA 1.000 23.000 2 UNITED_STATESM 1.000 23.000 3 UNITED_KINGDOM 0.913 21.000 4 CHINALINAATESM 0.870 20.000 5 BRAZILINAATESM 0.826 19.000 6 SPAINTANANDKIA 0.783 18.000 7 YUGOSLAVIATESM 0.783 18.000 8 ARGENTINAATESM 0.739 17.000 9 EGYPTORLOVAKIA 0.739 17.000 10 SWITZERLANDKIA 0.739 17.000 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): DIPLOMATIC_EXCHANGE
Rank Location Value 1 SYRIAERLANDKIA 0.968 2 MADAGASCARAKIA 0.929 3 PAKISTANANDKIA 0.929 4 HONDURASOVAKIA 0.903 5 ETHIOPIAOVAKIA 0.890 6 NEW_ZEALANDKIA 0.873 7 ECUADORLOVAKIA 0.865 8 THAILANDANDKIA 0.852 9 FINLANDAOVAKIA 0.850 10 LIBERIAIAVAKIA 0.844
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 JAPANLSIAVAKIA JAPANLSIAVAKIA UNITED_STATESM UNITED_STATESM JAPANLSIAVAKIA JAPANLSIAVAKIA JAPANLSIAVAKIA JAPANLSIAVAKIA 2 UNITED_STATESM UNITED_STATESM JAPANLSIAVAKIA JAPANLSIAVAKIA UNITED_STATESM UNITED_STATESM UNITED_STATESM UNITED_STATESM 3 UNITED_KINGDOM SWITZERLANDKIA SWITZERLANDKIA SWITZERLANDKIA UNITED_KINGDOM UNITED_KINGDOM SWITZERLANDKIA UNITED_KINGDOM 4 CHINALINAATESM UNITED_KINGDOM UNITED_KINGDOM UNITED_KINGDOM CHINALINAATESM CHINALINAATESM UNITED_KINGDOM CHINALINAATESM 5 EGYPTORLOVAKIA CHINALINAATESM CHINALINAATESM CHINALINAATESM BRAZILINAATESM BRAZILINAATESM CHINALINAATESM SWITZERLANDKIA 6 BRAZILINAATESM SPAINTANANDKIA SPAINTANANDKIA SPAINTANANDKIA EGYPTORLOVAKIA EGYPTORLOVAKIA SPAINTANANDKIA BRAZILINAATESM 7 ARGENTINAATESM ARGENTINAATESM EGYPTORLOVAKIA EGYPTORLOVAKIA SPAINTANANDKIA SPAINTANANDKIA ARGENTINAATESM SPAINTANANDKIA 8 SWITZERLANDKIA BRAZILINAATESM YUGOSLAVIATESM YUGOSLAVIATESM YUGOSLAVIATESM YUGOSLAVIATESM BRAZILINAATESM EGYPTORLOVAKIA 9 SPAINTANANDKIA EGYPTORLOVAKIA BRAZILINAATESM BRAZILINAATESM ARGENTINAATESM ARGENTINAATESM EGYPTORLOVAKIA YUGOSLAVIATESM 10 YUGOSLAVIATESM YUGOSLAVIATESM ARGENTINAATESM ARGENTINAATESM SWITZERLANDKIA SWITZERLANDKIA YUGOSLAVIATESM ARGENTINAATESM