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README.md

@@ -1,64 +1,124 @@
----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: algorithm
-    dtype: string
-  - name: edge_list
-    dtype: string
-  - name: node_count
-    dtype: int64
-  - name: edge_count
-    dtype: int64
-  - name: node_degree_node
-    dtype: int64
-  - name: node_degree
-    dtype: int64
-  - name: edge_existence_src
-    dtype: int64
-  - name: edge_existence_dst
-    dtype: int64
-  - name: edge_existence
-    dtype: bool
-  - name: cycle_check
-    dtype: bool
-  - name: triangle_count
-    dtype: int64
-  - name: connected_nodes_node
-    dtype: int64
-  - name: connected_nodes
-    dtype: string
-  - 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: 6311306
-    num_examples: 10000
-  - name: validation
-    num_bytes: 646652
-    num_examples: 1000
-  - name: test
-    num_bytes: 635077
-    num_examples: 1000
-  download_size: 1529568
-  dataset_size: 7593035
----
+---
+language: en
+license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- graph-ml
+tags:
+- graphs
+- synthetic
+- erdos-renyi
+- barabasi-albert
+- watts-strogatz
+- stochastic-block-model
+- complete-graph
+- graph-qa
+---
+
+# Graph Dataset with Task Labels
+
+A synthetic graph dataset containing graphs generated using five different algorithms, with pre-computed answers for 9 graph reasoning tasks.
+
+## Dataset Description
+
+This dataset contains synthetic graphs represented as edge lists, along with ground-truth answers for various graph reasoning tasks. The graphs are generated using five classical random graph models:
+
+- **Erdős–Rényi**: Random graphs where each edge is included independently with probability p
+- **Barabási–Albert**: Scale-free networks generated using preferential attachment
+- **Watts–Strogatz**: Small-world networks with high clustering and short path lengths
+- **Stochastic Block Model**: Community-structured graphs with 2-4 communities, higher edge probability within communities than between
+- **Complete Graph**: Fully connected graphs where every pair of nodes is connected
+
+## Dataset Structure
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `algorithm` | string | Graph generation algorithm: `erdos_renyi`, `barabasi_albert`, `watts_strogatz`, `stochastic_block_model`, or `complete` |
+| `edge_list` | string | Edge list in format `(u, v), (x, y), ...` |
+
+#### Task Columns
+
+| Task | Fields | 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 | Number of Examples |
+|-------|-------------------|
+| train | 10000 |
+| validation | 1000 |
+| test | 1000 |
+
+### Graph Statistics
+
+- **Node range**: [5, 25]
+- **Algorithms**: Balanced across all five types (cycled)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("vstenby/random-graphs")
+
+# 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']}")
+```
+
+### Converting to NetworkX
+
+```python
+import networkx as nx
+import ast
+
+def parse_edge_list(edge_list_str):
+    """Parse edge list string to list of tuples."""
+    if not edge_list_str:
+        return []
+    edges_str = f"[{edge_list_str}]"
+    return ast.literal_eval(edges_str)
+
+sample = dataset["train"][0]
+edges = parse_edge_list(sample["edge_list"])
+G = nx.Graph()
+G.add_nodes_from(range(sample["node_count"]))
+G.add_edges_from(edges)
+```
+
+## Generation Details
+
+- **Random seed**: 42 (train), 43 (validation), 44 (test)
+- **Generation method**: Each graph has a random number of nodes uniformly sampled from [5, 25]
+- **Task sampling**: For tasks requiring node/edge sampling (node_degree, edge_existence, connected_nodes, reachability, shortest_path), nodes are sampled uniformly at random
+
+## License
+
+MIT License
+
+MIT License