Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Riktad modularitetsanalys× | Betweenness Centrality× | |
|---|---|---|
| Ämnesområde | Nätverksanalys | Nätverksanalys |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2008 | 1977 |
| Upphovsperson≠ | Leicht, E. A. & Newman, M. E. J. | Freeman, L. C. |
| Typ≠ | Community detection / graph partitioning | Centrality measure |
| Ursprungskälla≠ | Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Alias | directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Närliggande≠ | 5 | 6 |
| Sammanfattning≠ | Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data. | 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. |
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