Vertaile menetelmiä
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| Painotettu kaksitilaverkkoanalyysi× | Modulaarianalyysi× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1997 (two-mode); weighted extensions 2000s | 2004 |
| Kehittäjä≠ | Borgatti, S. P. & Everett, M. G. | Newman, M. E. J. & Girvan, M. |
| Tyyppi≠ | Network structural analysis | Community detection / graph partitioning |
| Alkuperäislähde≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Rinnakkaisnimet | weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis. | 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. |
| ScholarGateAineisto ↗ |
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