Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Насочена детекция на общности× | Стохастичен блокови модел× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Година на възникване≠ | 2008 | 1983 |
| Създател≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | — |
| Тип≠ | Graph partitioning / modularity optimization | Probabilistic generative graph model |
| Основополагащ източник≠ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Други названия | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Свързани≠ | 6 | 7 |
| Резюме≠ | Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
| ScholarGateНабор от данни ↗ |
|
|