手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 動的指数型ランダムグラフモデル (TERGM / STERGM)× | 確率的ブロックモデル× | |
|---|---|---|
| 分野 | ネットワーク分析 | ネットワーク分析 |
| 系統≠ | Machine learning | Process / pipeline |
| 提唱年≠ | 2010–2014 | 1983 |
| 提唱者≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | — |
| 種類≠ | Probabilistic graphical model (temporal) | Probabilistic generative graph model |
| 原典≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 別名 | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 関連≠ | 4 | 7 |
| 概要≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | 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. |
| ScholarGateデータセット ↗ |
|
|