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README.md

@@ -1,58 +1,78 @@
----
-dataset_info:
-  features:
-  - name: algorithm
-    dtype: string
-  - name: edge_list
-    dtype: string
-  - name: node_count
-    dtype: int64
-  - name: edge_count
-    dtype: int64
-  - name: cycle_check
-    dtype: bool
-  - name: triangle_count
-    dtype: int64
-  - name: node_degree_node
-    dtype: int64
-  - name: node_degree
-    dtype: int64
-  - name: connected_nodes_node
-    dtype: int64
-  - name: connected_nodes
-    dtype: string
-  - name: edge_existence_src
-    dtype: int64
-  - name: edge_existence_dst
-    dtype: int64
-  - name: edge_existence
-    dtype: bool
-  - name: reachability_src
-    dtype: int64
-  - name: reachability_dst
-    dtype: int64
-  - name: reachability
-    dtype: bool
-  - name: shortest_path_src
-    dtype: int64
-  - name: shortest_path_dst
-    dtype: int64
-  - name: shortest_path
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 38654302
-    num_examples: 81841
-  - name: test
-    num_bytes: 1891565
-    num_examples: 4099
-  download_size: 11899101
-  dataset_size: 40545867
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+# Erdős Graph Dataset with Task Labels
+
+A graph dataset derived from [PKU-ML/Erdos](https://huggingface.co/datasets/PKU-ML/Erdos), 
+containing undirected graphs with pre-computed answers for 9 graph reasoning tasks.
+This dataset follows the same format as [vstenby/random-graphs](https://huggingface.co/datasets/vstenby/random-graphs).
+
+## Dataset Description
+
+This dataset contains graphs from the PKU-ML/Erdos benchmark, filtered to include only
+undirected graphs. Each graph has been processed to:
+- Convert edges to 0-indexed (for PyTorch Geometric compatibility)
+- Add pre-computed task columns matching the random-graphs format
+
+## Dataset Structure
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| algorithm | string | Always "erdos" |
+| edge_list | string | Edge list in format [(u, v), (x, y), ...] |
+
+#### Task Columns
+
+| Task | Columns | Description |
+|------|---------|-------------|
+| node_count | node_count (int) | Number of nodes in the graph |
+| edge_count | edge_count (int) | Number of edges in the graph |
+| node_degree | node_degree_node (int), node_degree (int) | Sampled node and its degree |
+| edge_existence | edge_existence_src (int), edge_existence_dst (int), edge_existence (bool) | Two sampled nodes and whether an edge exists between them |
+| cycle_check | cycle_check (bool) | Whether the graph contains a cycle |
+| triangle_counting | triangle_count (int) | Number of triangles in the graph |
+| connected_nodes | connected_nodes_node (int), connected_nodes (string) | Sampled node and comma-separated list of its neighbors |
+| reachability | reachability_src (int), reachability_dst (int), reachability (bool) | Two sampled nodes and whether a path exists between them |
+| shortest_path | shortest_path_src (int), shortest_path_dst (int), shortest_path (int) | Two sampled nodes and shortest path length (-1 if no path exists) |
+
+### Data Splits
+
+| Split | Examples |
+|-------|----------|
+| train | ~82,000 |
+| test | ~4,000 |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("vstenby/erdos")
+
+# Access a sample
+sample = dataset["train"][0]
+print(f"Algorithm: {sample['algorithm']}")
+print(f"Node count: {sample['node_count']}")
+print(f"Edge count: {sample['edge_count']}")
+print(f"Has cycle: {sample['cycle_check']}")
+print(f"Triangle count: {sample['triangle_count']}")
+
+# Node-specific tasks
+print(f"Node {sample['node_degree_node']} has degree {sample['node_degree']}")
+print(f"Node {sample['connected_nodes_node']} is connected to: {sample['connected_nodes']}")
+
+# Edge/path tasks
+print(f"Edge between {sample['edge_existence_src']} and {sample['edge_existence_dst']}: {sample['edge_existence']}")
+print(f"Path from {sample['reachability_src']} to {sample['reachability_dst']}: {sample['reachability']}")
+print(f"Shortest path from {sample['shortest_path_src']} to {sample['shortest_path_dst']}: {sample['shortest_path']}")
+```
+
+## Generation Details
+
+- **Source**: [PKU-ML/Erdos](https://huggingface.co/datasets/PKU-ML/Erdos)
+- **Filtering**: Only undirected graphs included; isomorphic_mapping task excluded
+- **Random seed**: 42 (train), 44 (test) for reproducible node/edge sampling
+- **Edge indexing**: Converted from 1-indexed to 0-indexed
+
+## License
+
+MIT License