SeldonIO/alibi: papers linking to it

GitHub · 2,651 stars · 269 forks · Python · created 2019 · github.com/SeldonIO/alibi

← Code and data repositories cited in research papers

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.

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#

20202025

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	1
Expert Systems with Applications	1
Neural Networks	1
Data Mining and Knowledge Discovery	1
Nature Machine Intelligence	1

Papers linking to SeldonIO/alibi, most cited first
Published	Title	Journal	Authors	Citations	Link
2020-10-13	Drug discovery with explainable artificial intelligence	Nature Machine Intelligence	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	Expert Systems with Applications	Alberto Barbado, Óscar Corcho, Richard Benjamins	77	cites it
2022-11-07	Counterfactual explanations as interventions in latent space	Data Mining and Knowledge Discovery	Riccardo Crupi, Alessandro Castelnovo, Daniele Regoli et al.	23	cites it
2025-06-02	Selecting Data Assets in Data Marketplaces	Business & Information Systems Engineering	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	Neural Networks	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

@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}
}

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