Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Starppriekšrocība (Betweenness Centrality)× | Modulāritātes analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1977 | 2004 |
| Autors≠ | Freeman, L. C. | Newman, M. E. J. & Girvan, M. |
| Tips≠ | Centrality measure | Community detection / graph partitioning |
| Pirmavots≠ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Citi nosaukumi | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
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