Vertaile menetelmiä
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| Modulaarianalyysi× | Kaksitilainen verkkotarkastelu× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2004 | 1974 |
| Kehittäjä≠ | Newman, M. E. J. & Girvan, M. | Breiger, R. L. |
| Tyyppi≠ | Community detection / graph partitioning | Bipartite graph analysis |
| Alkuperäislähde≠ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ | Breiger, R. L. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190. DOI ↗ |
| Rinnakkaisnimet | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity | bipartite network analysis, affiliation network analysis, two-mode SNA, dual-projection network analysis |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | Two-mode network analysis examines networks built from two distinct types of nodes — such as actors and events, authors and papers, or companies and board members — connected only across types. By analysing this bipartite structure directly or projecting it onto one-mode networks, researchers uncover affiliation patterns, shared memberships, and structural duality that are invisible in standard one-mode social network analysis. |
| ScholarGateAineisto ↗ |
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