方法对比
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| 社群检测× | 图注意力网络× | |
|---|---|---|
| 领域≠ | 网络分析 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 2002–2019 (algorithm family) | 2018 |
| 提出者≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | Veličković, P. et al. |
| 类型≠ | Graph-partitioning / clustering algorithm family | Graph neural network (attention-based) |
| 开创性文献≠ | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| 别名≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| 相关≠ | 5 | 4 |
| 摘要≠ | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGate数据集 ↗ |
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