| --- |
| 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_nodes |
| default: true |
| data_files: |
| - split: train |
| path: 1k/nodes.parquet |
| - config_name: 1k_edges |
| data_files: |
| - split: train |
| path: 1k/edges.parquet |
| - config_name: 10k_nodes |
| data_files: |
| - split: train |
| path: 10k/nodes.parquet |
| - config_name: 10k_edges |
| data_files: |
| - split: train |
| path: 10k/edges.parquet |
| - config_name: 100k_nodes |
| data_files: |
| - split: train |
| path: 100k/nodes.parquet |
| - config_name: 100k_edges |
| data_files: |
| - split: train |
| path: 100k/edges.parquet |
| - config_name: 1m_nodes |
| data_files: |
| - split: train |
| path: 1m/nodes.parquet |
| - config_name: 1m_edges |
| data_files: |
| - split: train |
| path: 1m/edges.parquet |
| - config_name: 10m_nodes |
| data_files: |
| - split: train |
| path: 10m/nodes.parquet |
| - config_name: 10m_edges |
| data_files: |
| - split: train |
| path: 10m/edges.parquet |
| --- |
| |
| # CS Knowledge Graph Dataset |
|
|
| [](https://creativecommons.org/licenses/by-sa/4.0/) |
| [](https://github.com/JugalGajjar/HyperComplEx-Multi-Space-KG-Embeddings) |
| [](https://ieeexplore.ieee.org/abstract/document/11400828) |
|
|
| 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 scale exposes two configs, `<scale>_nodes` and `<scale>_edges`. They |
| share a single split named `train` (a `datasets` convention — there is no |
| held-out test split, since the intended use is to define your own splits over |
| the graph). |
|
|
| ### `nodes` config |
|
|
| | 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` config |
|
|
| | 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) |
|
|
| **Dangling `CITES` targets.** Each scale is built from a Computer Science slice |
| of OpenAlex, so the `nodes` table only contains CS papers (plus their authors, |
| venues, and concepts). However, those CS papers may cite papers from outside |
| CS — those external papers appear as `target` in `CITES` edges but are **not** |
| present in the `nodes` table. Filter or add placeholder nodes as appropriate |
| for your task. Sources are always present in `nodes`; only `CITES` targets can |
| be dangling. |
|
|
| ## Usage |
|
|
| ### Load with the `datasets` library |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Configs follow the pattern "<scale>_nodes" / "<scale>_edges". |
| # Scales: 1k, 10k, 100k, 1m, 10m |
| nodes = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", "10k_nodes", split="train") |
| edges = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", "10k_edges", split="train") |
| |
| 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 numpy as np |
| import torch |
| from torch_geometric.data import HeteroData |
| from datasets import load_dataset |
| |
| scale = "10k" |
| nodes = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", f"{scale}_nodes", split="train").to_pandas() |
| edges = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", f"{scale}_edges", split="train").to_pandas() |
| |
| # Build per-type id -> contiguous index maps |
| 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) |
| |
| # Each node_id is prefixed with its type |
| 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]] |
| |
| # Drop CITES edges whose target isn't in the node set (cross-domain citations). |
| node_id_set = set(nodes["node_id"]) |
| edges = edges[edges["target"].isin(node_id_set)].reset_index(drop=True) |
| |
| 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(dtype=np.int64) |
| dst = group["target"].map(id_maps[dst_type]).to_numpy(dtype=np.int64) |
| data[src_type, relation, dst_type].edge_index = torch.from_numpy(np.stack([src, dst])) |
| |
| print(data) |
| ``` |
|
|
| ## 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", |
| ) |
| ``` |
|
|
| ## Citation |
|
|
| This dataset was introduced in the following paper. **If you use this dataset |
| in your work, please cite it.** Please also cite OpenAlex (the source data; |
| see their [citation guidance](https://docs.openalex.org)). |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @inproceedings{gajjar2025hypercomplex, |
| title={HyperComplEx: Adaptive Multi-Space Knowledge Graph Embeddings}, |
| author={Gajjar, Jugal and Ranaware, Kaustik and Subramaniakuppusamy, Kamalasankari and Gandhi, Vaibhav C}, |
| booktitle={2025 IEEE International Conference on Big Data (BigData)}, |
| pages={5623--5631}, |
| year={2025}, |
| organization={IEEE} |
| } |
| ``` |
|
|
| **APA:** |
|
|
| > Gajjar, J., Ranaware, K., Subramaniakuppusamy, K., & Gandhi, V. C. (2025, December). HyperComplEx: Adaptive Multi-Space Knowledge Graph Embeddings. In *2025 IEEE International Conference on Big Data (BigData)* (pp. 5623–5631). IEEE. |
|
|
| ## 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. |
|
|
| ## 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 |
| ``` |
|
|
