Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Detecția Comunităților× | Rețea Neuronală pe Grafuri× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2002–2019 (algorithm family) | 2017–2018 (major variants) |
| Autorul original≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | — |
| Tip≠ | Graph-partitioning / clustering algorithm family | Deep learning on graph-structured data |
| Sursa seminală≠ | 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 ↗ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). DOI ↗ |
| Denumiri alternative≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | GNN, GCN, GAT, GraphSAGE |
| Înrudite | 5 | 5 |
| Rezumat≠ | 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? | 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. |
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