Create README.md

README.md

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+---
+pretty_name: Stability-Landscape
+license: cc-by-4.0
+language:
+- en
+tags:
+- graph-neural-networks
+- kuramoto-oscillators
+- basin-stability
+- power-grids
+- physics
+- long-range-dependencies
+size_categories:
+- 100M<n<1B
+datasets:
+- name: KBL
+  annotation_type: synthetic
+  source_datasets: []
+task_categories:
+- graph-ml
+- graph-regression
+- image-generation
+---
+
+# Kuramoto-Stability-Landscape (KSL)
+
+**Kuramoto-Basin-Landscape (KSL)** provides per-node **basin-stability heat-maps** for two ensembles of second-order Kuramoto oscillator networks.  
+The data are designed as a benchmark for *graph-to-image regression* and for studying how Graph Neural Networks capture long-range dependencies that arise in real-world synchronisation problems such as power-grid dynamics.
+
+| Sub-dataset | # Graphs | Nodes / graph | Files | Heat-maps / file | Resolution |
+|-------------|---------:|--------------:|------:|-----------------:|-----------:|
+| `dataset20` | 10 000   | 20            | 10 000 **HDF5** | 40 (20×`basin_heatmap_i` + 20×`samples_heatmap_i`) | 20 × 20 |
+| `dataset100`| 10 000   | 100           | 10 000 **HDF5** | 200 (100×`basin_heatmap_i` + 100×`samples_heatmap_i`) | 20 × 20 |
+
+The archive **`num_sections_20.tar`** contains two main directories within a single compressed `.tar` file:
+num_sections_20/ ├── ds20/ │ ├── heatmap_grid_00001.h5 │ ├── heatmap_grid_00002.h5 │ └── ... └── ds100/ ├── heatmap_grid_00001.h5 ├── heatmap_grid_00002.h5 └── ...
+
+
+Each `.h5` file represents one unique oscillator network and includes:
+
+- **`basin_heatmap_i`** (target variable):  
+  - Continuous stability landscape representing dynamic stability intensity (values in [0, 1]).  
+  - Shape: `(20, 20)` per node.
+
+- **`samples_heatmap_i`** (auxiliary information):  
+  - Number of Monte-Carlo perturbation samples per heatmap cell.
+  - Shape: `(20, 20)` per node.
+
+- `i` corresponds to node indices:
+  - Nodes 1 to 20 for **dataset20**.
+  - Nodes 1 to 100 for **dataset100**.
+
+### Examples:
+
+- **`dataset20`**:  
+  Each file contains **40 heatmaps** (`20 basin_heatmap_X` + `20 samples_heatmap_X`).
+
+- **`dataset100`**:  
+  Each file contains **200 heatmaps** (`100 basin_heatmap_X` + `100 samples_heatmap_X`).
+
+---
+
+## 🚀 Intended Tasks
+
+This dataset introduces a novel machine-learning task:
+
+- **Graph-to-Image Regression**:  
+  Predicting detailed SNBS heatmap landscapes directly from graph topology and nodal attributes.
+
+### Downstream Application
+
+- Single-node basin stability (SNBS) probability prediction.
+- Stability analysis and robustness assessment of dynamical networks.
+
+---
+
+## 🧪 Data Splits
+
+Each ensemble is pre-split into:
+
+- **Training set**: 70%
+- **Validation set**: 15%
+- **Test set**: 15%
+
+These splits enable consistent benchmarking and out-of-distribution evaluation.
+
+---
+
+## ⚙️ Generation Methodology
+
+- **Underlying Dynamical Model**: Second-order Kuramoto oscillators.
+- **Perturbations**: Monte Carlo sampled perturbations `(φ, φ̇)` applied per node.
+- **Heatmap Computation**: Stability status (continuous stability value) and perturbation density computed per 20x20 spatial bins from raw simulation outcomes.
+
+The dataset was generated using extensive computational resources (>500,000 CPU hours).
+
+---
+
+## 📖 Usage and Loading
+
+To load and access data conveniently, first unpack the provided `.tar` file:
+
+```bash
+tar -xvf num_sections_20.tar
+
+Then, for example, load .h5 files in Python:
+
+```python
+with h5py.File('num_sections_20/ds20/heatmap_grid_00001.h5', 'r') as f:
+    basin_heatmap_node1 = np.array(f['basin_heatmap_1'])
+    samples_heatmap_node1 = np.array(f['samples_heatmap_1'])
+print(basin_heatmap_node1.shape)  # (20, 20)