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README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+language:
+- en
+pretty_name: CS Knowledge Graph (OpenAlex)
+size_categories:
+- 10M<n<100M
+task_categories:
+- graph-ml
+- feature-extraction
+tags:
+- knowledge-graph
+- openalex
+- computer-science
+- bibliographic
+- citation-network
+- co-authorship
+- scholarly
+- link-prediction
+- node-classification
+configs:
+- config_name: 1k
+  data_files:
+  - split: nodes
+    path: 1k/nodes.parquet
+  - split: edges
+    path: 1k/edges.parquet
+- config_name: 10k
+  data_files:
+  - split: nodes
+    path: 10k/nodes.parquet
+  - split: edges
+    path: 10k/edges.parquet
+- config_name: 100k
+  data_files:
+  - split: nodes
+    path: 100k/nodes.parquet
+  - split: edges
+    path: 100k/edges.parquet
+- config_name: 1m
+  data_files:
+  - split: nodes
+    path: 1m/nodes.parquet
+  - split: edges
+    path: 1m/edges.parquet
+- config_name: 10m
+  data_files:
+  - split: nodes
+    path: 10m/nodes.parquet
+  - split: edges
+    path: 10m/edges.parquet
+---
+
+# CS Knowledge Graph Dataset (OpenAlex)
+
+A multi-scale heterogeneous knowledge graph of Computer Science scholarly data,
+built from [OpenAlex](https://openalex.org). Each scale is an independent,
+self-contained subgraph centered on Computer Science papers, their authors,
+publication venues, and concept tags, plus the relationships between them.
+
+The dataset is intended for research on knowledge graph embeddings, link
+prediction, node classification, scholarly recommendation, and graph neural
+networks at varying scales of compute.
+
+## Scales
+
+Five scales are provided so the same pipeline can be benchmarked from quick
+prototyping (1k) to large-scale training (10m). Each scale is a strict superset
+of the smaller ones in spirit, but is sampled independently — treat them as
+five separate graphs rather than nested cuts.
+
+| Config | Nodes      | Edges       | Parquet size | Raw SQLite (zip) |
+|--------|-----------:|------------:|-------------:|-----------------:|
+| `1k`   |      5,237 |      32,655 |       277 KB |           961 KB |
+| `10k`  |     44,933 |     252,631 |       2.0 MB |           7.7 MB |
+| `100k` |    348,983 |   2,162,386 |        16 MB |            68 MB |
+| `1m`   |  2,384,896 |  13,530,177 |       117 MB |           597 MB |
+| `10m`  |  7,210,506 |  44,631,484 |       384 MB |           2.1 GB |
+
+## Schema
+
+Each config has two splits, named `nodes` and `edges` (rather than the usual
+train/test/validation, since this is a graph dataset).
+
+### `nodes` split
+
+| Column       | Type   | Description                                                           |
+|--------------|--------|-----------------------------------------------------------------------|
+| `node_id`    | string | Unique node identifier, prefixed by type (e.g. `paper_W2604738573`).  |
+| `node_name`  | string | Human-readable name (paper title, author display name, venue, etc.).  |
+| `node_type`  | string | One of `Paper`, `Author`, `Venue`, `Concept`.                         |
+| `attributes` | string | Type-specific attributes encoded as a JSON string (see below).        |
+
+The `attributes` JSON object has different keys depending on `node_type`:
+
+- **Paper**: `year` (int), `citation_count` (int), `venue` (string), `type` (string, e.g. `article`)
+- **Author**: `h_index` (int or null), `citation_count` (int or null), `works_count` (int or null), `institution` (string)
+- **Venue**: `type` (string, e.g. `journal`, `conference`), `publisher` (string)
+- **Concept**: `domain` (string, e.g. `CS`)
+
+### `edges` split
+
+| Column     | Type   | Description                                                                                |
+|------------|--------|--------------------------------------------------------------------------------------------|
+| `source`   | string | `node_id` of the source node.                                                              |
+| `relation` | string | One of `AUTHORED`, `CITES`, `PUBLISHED_IN`, `BELONGS_TO`, `COLLABORATES_WITH`.             |
+| `target`   | string | `node_id` of the target node.                                                              |
+| `year`     | float  | Year associated with the edge when applicable (e.g. publication year); `null` otherwise.   |
+
+Relation semantics:
+
+- `AUTHORED` — `Author → Paper`
+- `CITES` — `Paper → Paper`
+- `PUBLISHED_IN` — `Paper → Venue`
+- `BELONGS_TO` — `Paper → Concept`
+- `COLLABORATES_WITH` — `Author → Author` (co-authorship; symmetric, may appear in both directions)
+
+## Usage
+
+### Load with the `datasets` library
+
+```python
+from datasets import load_dataset
+
+# Pick a scale: "1k", "10k", "100k", "1m", or "10m"
+nodes = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", "10k", split="nodes")
+edges = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", "10k", split="edges")
+
+print(nodes[0])
+# {'node_id': 'paper_W...', 'node_name': '...', 'node_type': 'Paper',
+#  'attributes': '{"year": 2016, "citation_count": 1816, ...}'}
+
+import json
+attrs = json.loads(nodes[0]["attributes"])
+```
+
+### Load directly with pandas / pyarrow
+
+```python
+import pandas as pd
+nodes = pd.read_parquet("hf://datasets/jugalgajjar/CS-Knowledge-Graph-Dataset/100k/nodes.parquet")
+edges = pd.read_parquet("hf://datasets/jugalgajjar/CS-Knowledge-Graph-Dataset/100k/edges.parquet")
+```
+
+### Build a PyTorch Geometric graph
+
+```python
+import json
+import torch
+from torch_geometric.data import HeteroData
+from datasets import load_dataset
+
+scale = "10k"
+nodes = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", scale, split="nodes").to_pandas()
+edges = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", scale, split="edges").to_pandas()
+
+data = HeteroData()
+id_maps = {}
+for ntype, group in nodes.groupby("node_type"):
+    ids = group["node_id"].tolist()
+    id_maps[ntype] = {nid: i for i, nid in enumerate(ids)}
+    data[ntype].num_nodes = len(ids)
+
+# infer source/target type from the node_id prefix
+type_from_prefix = {"paper": "Paper", "author": "Author", "venue": "Venue", "concept": "Concept"}
+def ntype_of(nid: str) -> str:
+    return type_from_prefix[nid.split("_", 1)[0]]
+
+for relation, group in edges.groupby("relation"):
+    src_type = ntype_of(group["source"].iloc[0])
+    dst_type = ntype_of(group["target"].iloc[0])
+    src = group["source"].map(id_maps[src_type]).to_numpy()
+    dst = group["target"].map(id_maps[dst_type]).to_numpy()
+    data[src_type, relation, dst_type].edge_index = torch.tensor([src, dst], dtype=torch.long)
+```
+
+## Raw SQLite databases
+
+In addition to the Parquet files, the original SQLite databases used to build
+each scale are available under `raw/`:
+
+```
+raw/cs1k_openalex.db.zip
+raw/cs10k_openalex.db.zip
+raw/cs100k_openalex.db.zip
+raw/cs1m_openalex.db.zip
+raw/cs10m_openalex.db.zip
+```
+
+These are useful if you want to run SQL queries over the source records
+directly. Download with `huggingface_hub`:
+
+```python
+from huggingface_hub import hf_hub_download
+path = hf_hub_download(
+    repo_id="jugalgajjar/CS-Knowledge-Graph-Dataset",
+    repo_type="dataset",
+    filename="raw/cs10k_openalex.db.zip",
+)
+```
+
+## Source and licensing
+
+- **Source data:** [OpenAlex](https://openalex.org), released into the public
+  domain under [CC0](https://creativecommons.org/publicdomain/zero/1.0/).
+- **This derived dataset:** licensed under
+  [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). You may use,
+  modify, and redistribute it, including commercially, provided you give
+  attribution and license your derivative works under the same terms.
+- Please also cite OpenAlex when using this dataset; see their
+  [citation guidance](https://docs.openalex.org).
+
+## Repository layout
+
+```
+.
+├── README.md
+├── 1k/
+│   ├── nodes.parquet
+│   └── edges.parquet
+├── 10k/    (same layout)
+├── 100k/   (same layout)
+├── 1m/     (same layout)
+├── 10m/    (same layout)
+└── raw/
+    ├── cs1k_openalex.db.zip
+    ├── cs10k_openalex.db.zip
+    ├── cs100k_openalex.db.zip
+    ├── cs1m_openalex.db.zip
+    └── cs10m_openalex.db.zip
+```

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