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CS-Knowledge-Graph-Dataset

Tasks:
Graph Machine Learning
Feature Extraction
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
knowledge-graph
openalex
computer-science
bibliographic
citation-network
co-authorship
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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CS-Knowledge-Graph-Dataset / README.md
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Add citation for HyperComplEx (Gajjar et al., 2025) — paper that introduced this dataset
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metadata 
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 (OpenAlex)

A multi-scale heterogeneous knowledge graph of Computer Science scholarly data, built from OpenAlex. 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 

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 

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 

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:

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).

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, released into the public domain under CC0.
  • This derived dataset: licensed under CC 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