Add ESTGEL EAM and model layers#56
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Introduce core ESTGEL components: two new modules implementing adjacency construction and the PyTorch model layers. src/estgel_eam.py provides helpers to build dense adjacency matrices from active cells or sparse edge lists, compute node importance, and generate nested subgraph tensors used by the EAM decomposition. src/estgel_layers.py implements the Edge Attention Aggregation (EAM), Dynamic Relation Learning (DRL), Dynamic Node Learning (DNL) blocks, timestep/block orchestration, PyG Batch handling, timestep selection, and a full ESTGELClassifier readout. These additions wire NumPy/Pandas preprocessing into Torch/PyG workflows and provide named outputs and utilities for training/inference on spatio-temporal graph data.
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Introduce core ESTGEL components: two new modules implementing adjacency construction and the PyTorch model layers. src/estgel_eam.py provides helpers to build dense adjacency matrices from active cells or sparse edge lists, compute node importance, and generate nested subgraph tensors used by the EAM decomposition.
src/estgel_layers.py implements the Edge Attention Aggregation (EAM), Dynamic Relation Learning (DRL), Dynamic Node Learning (DNL) blocks, timestep/block orchestration, PyG Batch handling, timestep selection, and a full ESTGELClassifier readout.
These additions wire NumPy/Pandas preprocessing into Torch/PyG workflows and provide named outputs and utilities for training/inference on spatio-temporal graph data.