---
title: "SeldonIO/alibi: papers citing this GitHub repository"
description: "5 papers in the tracked journals link to the GitHub repository SeldonIO/alibi in their Crossref records, the first in 2020; most of them (2) in 2025."
url: https://scholar4ai.com/code/github-seldonio-alibi
markdown_url: https://scholar4ai.com/code/github-seldonio-alibi.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the GitHub repository SeldonIO/alibi in their Crossref records, the first in 2020; most of them (2) in 2025. (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)
---

# SeldonIO/alibi: papers linking to it

Research papers in the 480 tracked journals that link to SeldonIO/alibi (https://github.com/SeldonIO/alibi), 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
- 2,651 stars
- 269 forks
- Python
- created 2019
- https://github.com/SeldonIO/alibi

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

## Key findings

- 5 papers in the tracked journals link to the GitHub repository SeldonIO/alibi in their Crossref records, the first in 2020; most of them (2) in 2025.
- By category: AI (4) and Information Systems (1); the journals with the most are Business & Information Systems Engineering (1), Expert Systems with Applications (1) and Neural Networks (1).

## By year

*Papers linking to SeldonIO/alibi by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 1 |
| 2025 | 2 |

## Journals

*Journals whose papers link to SeldonIO/alibi most*

| Journal | Papers |
|---|---:|
| [Business & Information Systems Engineering](https://scholar4ai.com/journals/business-information-systems-engineering.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |

## Papers

*Papers linking to SeldonIO/alibi, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-10-13 | [Drug discovery with explainable artificial intelligence](https://doi.org/10.1038/s42256-020-00236-4) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider | 1,061 | cites it |
| 2021-10-25 | [Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM](https://doi.org/10.1016/j.eswa.2021.116100) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Alberto Barbado, Óscar Corcho, Richard Benjamins | 77 | cites it |
| 2022-11-07 | [Counterfactual explanations as interventions in latent space](https://doi.org/10.1007/s10618-022-00889-2) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Riccardo Crupi, Alessandro Castelnovo, Daniele Regoli et al. | 23 | cites it |
| 2025-06-02 | [Selecting Data Assets in Data Marketplaces](https://doi.org/10.1007/s12599-025-00940-8) | [Business & Information Systems Engineering](https://scholar4ai.com/journals/business-information-systems-engineering.md) | Dominik Martin, Daniel Heinz, Moritz Glauner et al. | 7 | cites it |
| 2025-11-03 | [Achieving faithful explainability in feedforward neural networks through accurately computed feature attribution](https://doi.org/10.1016/j.neunet.2025.108277) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Jose L. Carles-Bou, Enrique J. Carmona | 5 | 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). SeldonIO/alibi: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-seldonio-alibi

```bibtex
@misc{scholar4ai-code-github-seldonio-alibi,
  author = {{Scholar4AI}},
  title = {SeldonIO/alibi: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-seldonio-alibi}},
  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/).