Calculates common social network measures on each selected input network.
Network agent x agent
Block Model - Newman's Clustering Algorithm
Network Level Measures
Measure Value Row count 31163.000 Column count 31163.000 Link count 120029.000 Density 0.000 Components of 1 node (isolates) 703 Components of 2 nodes (dyadic isolates) 415 Components of 3 or more nodes 481 Reciprocity 1.000 Characteristic path length 2.017 Clustering coefficient 0.632 Network levels (diameter) 28.076 Network fragmentation 0.220 Krackhardt connectedness 0.780 Krackhardt efficiency 1.000 Krackhardt hierarchy 0.000 Krackhardt upperboundedness 1.000 Degree centralization 0.000 Betweenness centralization 0.052 Closeness centralization 0.000 Reciprocal (symmetric)? Yes
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.000 0.000 0.000 Total degree centrality [Unscaled] 0.000 145.000 4.054 6.711 In-degree centrality 0.000 0.000 0.000 0.000 In-degree centrality [Unscaled] 0.000 145.000 4.054 6.711 Out-degree centrality 0.000 0.000 0.000 0.000 Out-degree centrality [Unscaled] 0.000 145.000 4.054 6.711 Closeness centrality 0.000 0.000 0.000 0.000 Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 In-Closeness centrality 0.000 0.000 0.000 0.000 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.052 0.000 0.001 Betweenness centrality [Unscaled] 0.000 25281108.000 81811.976 431394.224 Clique membership count 0.000 157.000 2.688 5.599 Simmelian ties 0.000 0.006 0.000 0.000 Simmelian ties [Unscaled] 0.000 202.000 7.396 10.368 Clustering coefficient 0.000 1.000 0.632 0.390
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: 31163, density: 0.000247202)
Rank Agent Value Unscaled Context* 1 SARMA, SD 0.000 145.000 -1.291 2 PARISI, G 0.000 139.000 -1.353 3 STANLEY, HE 0.000 136.000 -1.384 4 MACDONALD, AH 0.000 135.000 -1.394 5 SORNETTE, D 0.000 113.000 -1.619 6 SCHEFFLER, M 0.000 110.000 -1.650 7 BOUCHAUD, JP 0.000 100.000 -1.752 8 PEETERS, FM 0.000 90.000 -1.855 9 BEENAKKER, CWJ 0.000 89.000 -1.865 10 PFEIFFER, LN 0.000 86.000 -1.895 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.000 Mean in random network: 0.000 Std.dev: 0.000 Std.dev in random network: 0.000 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 SARMA, SD 0.000 145.000 2 PARISI, G 0.000 139.000 3 STANLEY, HE 0.000 136.000 4 MACDONALD, AH 0.000 135.000 5 SORNETTE, D 0.000 113.000 6 SCHEFFLER, M 0.000 110.000 7 BOUCHAUD, JP 0.000 100.000 8 PEETERS, FM 0.000 90.000 9 BEENAKKER, CWJ 0.000 89.000 10 PFEIFFER, LN 0.000 86.000 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 SARMA, SD 0.000 145.000 2 PARISI, G 0.000 139.000 3 STANLEY, HE 0.000 136.000 4 MACDONALD, AH 0.000 135.000 5 SORNETTE, D 0.000 113.000 6 SCHEFFLER, M 0.000 110.000 7 BOUCHAUD, JP 0.000 100.000 8 PEETERS, FM 0.000 90.000 9 BEENAKKER, CWJ 0.000 89.000 10 PFEIFFER, LN 0.000 86.000 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: 31163, density: 0.000247202)
Rank Agent Value Unscaled Context* 1 All nodes have this value 0.000 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.000 Mean in random network: 104.745 Std.dev: 0.000 Std.dev in random network: 98.341 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 All nodes have this value 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: agent x agent (size: 31163, density: 0.000247202)
Rank Agent Value Unscaled Context* 1 REVCOLEVSCHI, A 0.052 25281108.000 0.003 2 UCHIDA, S 0.038 18369268.000 0.002 3 EISAKI, H 0.036 17575768.000 0.002 4 BOUCHAUD, JP 0.027 12924963.000 0.002 5 CHEONG, SW 0.024 11682493.000 0.002 6 RONNOW, HM 0.023 11086268.000 0.001 7 WANG, Y 0.022 10763784.000 0.001 8 UEDA, Y 0.022 10697646.000 0.001 9 NAKATSUJI, S 0.021 10359362.000 0.001 10 SCHIFFER, P 0.018 8913411.000 0.001 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.000 Mean in random network: -0.002 Std.dev: 0.001 Std.dev in random network: 16.675 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 UCHIDA, S 157.000 2 EISAKI, H 154.000 3 SARRAO, JL 139.000 4 CHEONG, SW 136.000 5 SHIRANE, G 130.000 6 TOKURA, Y 129.000 7 SHEN, ZX 122.000 8 REVCOLEVSCHI, A 118.000 9 PAGLIUSO, PG 109.000 10 STANLEY, HE 108.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): agent x agent
Rank Agent Value Unscaled 1 EISAKI, H 0.006 202.000 2 UCHIDA, S 0.006 195.000 3 REVCOLEVSCHI, A 0.006 193.000 4 UEDA, Y 0.006 182.000 5 CHEONG, SW 0.006 176.000 6 CANFIELD, PC 0.005 167.000 7 SARRAO, JL 0.005 167.000 8 LEE, SI 0.005 166.000 9 TOKURA, Y 0.005 159.000 10 TAJIMA, S 0.005 146.000 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 HAVILIO, M 1.000 2 FENG, YP 1.000 3 JACQUOD, PRJ 1.000 4 LAROCHELLE, DA 1.000 5 GARRISON, JE 1.000 6 NAGARAJ, B 1.000 7 MUTO, S 1.000 8 IGAMI, M 1.000 9 OKADA, S 1.000 10 NAKADA, K 1.000
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 REVCOLEVSCHI, A CHOU, T - - SARMA, SD CHOU, T SARMA, SD SARMA, SD 2 UCHIDA, S KIM, KS - - PARISI, G KIM, KS PARISI, G PARISI, G 3 EISAKI, H OSTER, G - - STANLEY, HE OSTER, G STANLEY, HE STANLEY, HE 4 BOUCHAUD, JP COLEMAN, P - - MACDONALD, AH COLEMAN, P MACDONALD, AH MACDONALD, AH 5 CHEONG, SW PEPIN, C - - SORNETTE, D PEPIN, C SORNETTE, D SORNETTE, D 6 RONNOW, HM TSVELIK, AM - - SCHEFFLER, M TSVELIK, AM SCHEFFLER, M SCHEFFLER, M 7 WANG, Y VISHWANATH, A - - BOUCHAUD, JP VISHWANATH, A BOUCHAUD, JP BOUCHAUD, JP 8 UEDA, Y SENTHIL, T - - PEETERS, FM SENTHIL, T PEETERS, FM PEETERS, FM 9 NAKATSUJI, S YAMAMOTO, S - - BEENAKKER, CWJ YAMAMOTO, S BEENAKKER, CWJ BEENAKKER, CWJ 10 SCHIFFER, P FUKUI, T - - PFEIFFER, LN FUKUI, T PFEIFFER, LN PFEIFFER, LN