Claude
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0b6b2f8353
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docs: Add comprehensive GNN latent space research documentation
Research covering Graph Neural Network implementation focusing on
latent space-graph reality interplay:
- gnn-architecture-analysis.md: Current RuVector GNN architecture deep-dive
- RuvectorLayer structure, message passing, multi-head attention, GRU
- Mathematical formulations and complexity analysis
- attention-mechanisms-research.md: Alternative attention mechanisms
- Edge-featured attention (GAT extensions)
- Hyperbolic attention for hierarchical graphs
- Sparse attention (Local+Global for HNSW layers)
- Linear attention (Performer, O(n) complexity)
- RoPE for distance encoding, Flash Attention
- Mixture of Experts, Cross-attention dual-space
- latent-graph-interplay.md: Core bridging research
- Manifold hypothesis for graphs
- Geometric structure (Euclidean vs Hyperbolic)
- Encoding/decoding strategies
- Information-theoretic perspective (DGI, IB)
- Contrastive learning for alignment
- Spectral methods and disentanglement
- optimization-strategies.md: Training strategies
- Loss function taxonomy
- Hard negative sampling
- Curriculum learning and meta-learning
- Multi-objective optimization
- advanced-architectures.md: Cutting-edge approaches
- Graph Transformers (Graphormer, GPS)
- Hyperbolic GNNs, Neural ODEs
- Equivariant networks, Generative models
- implementation-roadmap.md: 12-month practical plan
- Priority framework and benchmarking
- Phase-by-phase implementation guide
- Risk mitigation and success metrics
Total: ~160KB of research across 6 documents
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2025-11-30 02:36:07 +00:00 |
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