---
title: "stellargraph/stellargraph: papers citing it"
description: "9 papers in the tracked journals link to the GitHub repository stellargraph/stellargraph in their Crossref records, the first in 2021; most of them (2) in 2024."
url: https://scholar4ai.com/code/github-stellargraph-stellargraph
markdown_url: https://scholar4ai.com/code/github-stellargraph-stellargraph.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "9 papers in the tracked journals link to the GitHub repository stellargraph/stellargraph in their Crossref records, the first in 2021; most of them (2) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# stellargraph/stellargraph: papers linking to it

Research papers in the 480 tracked journals that link to stellargraph/stellargraph (https://github.com/stellargraph/stellargraph), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 3,064 stars
- 441 forks
- Python
- license Apache-2.0
- created 2018
- https://github.com/stellargraph/stellargraph

All repositories: https://scholar4ai.com/code.md

## Key findings

- 9 papers in the tracked journals link to the GitHub repository stellargraph/stellargraph in their Crossref records, the first in 2021; most of them (2) in 2024.
- By category: AI (6), Finance (1) and Information Systems (1); the journals with the most are Expert Systems with Applications (2), Neural Computing and Applications (2) and Data Mining and Knowledge Discovery (2).

## By year

*Papers linking to stellargraph/stellargraph by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 2 |
| 2024 | 2 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to stellargraph/stellargraph most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 2 |
| [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | 1 |
| [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | 1 |

## Papers

*Papers linking to stellargraph/stellargraph, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-09-05 | [A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns](https://doi.org/10.1016/j.eswa.2022.118721) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu et al. | 98 | cites it |
| 2023-10-05 | [A causal-temporal graphic convolutional network (CT-GCN) approach for TBM load prediction in tunnel excavation](https://doi.org/10.1016/j.eswa.2023.121977) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Xianlei Fu, Yue Pan, Limao Zhang | 68 | cites it |
| 2022-08-07 | [Detection of fake news campaigns using graph convolutional networks](https://doi.org/10.1016/j.jjimei.2022.100104) | [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | Dimitrios Michail, Nikos Kanakaris, Iraklis Varlamis | 32 | cites it |
| 2023-12-06 | [An entity-guided text summarization framework with relational heterogeneous graph neural network](https://doi.org/10.1007/s00521-023-09247-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Jingqiang Chen | 12 | cites it |
| 2021-08-05 | [Link prediction in dynamic networks using random dot product graphs](https://doi.org/10.1007/s10618-021-00784-2) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Francesco Sanna Passino, Anna S. Bertiger, Joshua Neil et al. | 10 | cites it |
| 2024-05-14 | [Network analytics for insurance fraud detection: a critical case study](https://doi.org/10.1007/s13385-024-00384-6) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Bruno Deprez, Félix Vandervorst, Wouter Verbeke et al. | 8 | cites it |
| 2024-05-20 | [Multi-class vulnerability prediction using value flow and graph neural networks](https://doi.org/10.1007/s00521-024-09819-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Connor McLaughlin, Yi Lu | 6 | cites it |
| 2025-10-25 | [Real-time OD connection demand forecasting based on streaming temporal link prediction in dynamic graphs](https://doi.org/10.1016/j.trc.2025.105413) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Yibin Yang, Hao Li, Huizhao Tu | 2 | cites it |
| 2026-08-04 | [Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection](https://doi.org/10.1007/s10618-026-01249-0) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Félix Vandervorst, Bruno Deprez, Wouter Verbeke et al. | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). stellargraph/stellargraph: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-stellargraph-stellargraph

```bibtex
@misc{scholar4ai-code-github-stellargraph-stellargraph,
  author = {{Scholar4AI}},
  title = {stellargraph/stellargraph: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-stellargraph-stellargraph}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).