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| 그래프 신경망× | 스펙트럼 군집화× | |
|---|---|---|
| 분야≠ | 네트워크 분석 | 머신러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 2017–2018 (major variants) | 2002 |
| 창시자≠ | — | Ng, A. Y.; Jordan, M. I.; Weiss, Y. |
| 유형≠ | Deep learning on graph-structured data | Graph-based clustering (spectral method) |
| 원전≠ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). DOI ↗ | Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. link ↗ |
| 별칭 | GNN, GCN, GAT, GraphSAGE | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering |
| 관련 | 5 | 5 |
| 요약≠ | A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes. | Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate. |
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