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LICENSE

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+CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
+
+The person who associated a work with this deed has dedicated the work to the
+public domain by waiving all of his or her rights to the work worldwide under
+copyright law, including all related and neighboring rights, to the extent
+allowed by law.
+
+You can copy, modify, distribute and perform the work, even for commercial
+purposes, all without asking permission.
+
+For more information: https://creativecommons.org/publicdomain/zero/1.0/

README.md

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----
-license: cc0-1.0
----
+---
+license: cc0-1.0
+task_categories:
+  - tabular-classification
+size_categories:
+  - 1K<n<10K
+---
+
+# Graph Topology Benchmark
+
+Synthetic graph topology data for network analysis.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("network-robustness-team/graph-topology-benchmark")
+df = dataset["train"].to_pandas()
+```
+
+Or use the provided loader:
+
+```python
+from loader import load_data
+
+df = load_data()
+```
+
+## Schema
+
+### Metrics
+
+| Column | Type | Description |
+|--------|------|-------------|
+| path_redundancy | float | Normalized metric |
+| clustering_coefficient | float | Normalized metric |
+| capacity_margin | float | Normalized metric |
+| robustness_score | float | Normalized metric |
+
+### Categorical Variables
+
+| Column | Type | Description |
+|--------|------|-------------|
+| topology | string | Categorical variable |
+| node_count_range | string | Categorical variable |
+
+## Statistics
+
+- **Rows**: 1000
+- **Columns**: 6
+
+## License
+
+CC0 1.0 Universal (Public Domain)
+
+---
+
+*Note: The network breathes through its many lungs.*

data/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e6ae2696ab7f4413c4d4ae3a426f4f3e2dcebb5c0e716e6bebbadd715563a19
+size 41577

loader.py

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+"""
+Data loader for graph-topology-benchmark
+"""
+
+import pandas as pd
+from pathlib import Path
+
+
+def load_data(split: str = "train") -> pd.DataFrame:
+    """
+    Load the dataset.
+    
+    Args:
+        split: Data split ("train" by default)
+    
+    Returns:
+        DataFrame with the loaded data
+    """
+    data_dir = Path(__file__).parent / "data"
+    parquet_path = data_dir / f"{split}.parquet"
+    
+    if not parquet_path.exists():
+        raise FileNotFoundError(f"Data file not found: {parquet_path}")
+    
+    return pd.read_parquet(parquet_path)
+
+
+def get_columns() -> list:
+    """Return list of column names."""
+    return ['path_redundancy', 'clustering_coefficient', 'capacity_margin', 'robustness_score']
+
+
+if __name__ == "__main__":
+    df = load_data()
+    print(f"Loaded {len(df)} rows")
+    print(df.head())

schema.json

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+{
+  "schema_version": "1.0",
+  "columns": {
+    "path_redundancy": {
+      "type": "float",
+      "range": [
+        0.01919646196940442,
+        0.9911878938245318
+      ]
+    },
+    "clustering_coefficient": {
+      "type": "float",
+      "range": [
+        0.005663291229804239,
+        0.9490426666419055
+      ]
+    },
+    "capacity_margin": {
+      "type": "float",
+      "range": [
+        0.07957279575388329,
+        0.989321782672835
+      ]
+    },
+    "robustness_score": {
+      "type": "float",
+      "range": [
+        0.0,
+        0.5826344421486561
+      ]
+    },
+    "topology": {
+      "type": "string",
+      "values": [
+        "mesh",
+        "tree",
+        "hybrid",
+        "star",
+        "ring"
+      ]
+    },
+    "node_count_range": {
+      "type": "string",
+      "values": [
+        "1000-10000",
+        "100-1000",
+        "10-100",
+        "10000+"
+      ]
+    }
+  },
+  "note": "Normalized metrics for cross-domain analysis"
+}