Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modulāritātes analīze× | Īpašvektoru centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2004 | 1972 |
| Autors≠ | Newman, M. E. J. & Girvan, M. | Bonacich, P. |
| Tips≠ | Community detection / graph partitioning | Centrality measure |
| Pirmavots≠ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Citi nosaukumi | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
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