TorchSpatiotemporal/tsl: papers linking to it

GitHub · 388 stars · 37 forks · Python · license MIT · created 2022 · github.com/TorchSpatiotemporal/tsl

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to TorchSpatiotemporal/tsl (https://github.com/TorchSpatiotemporal/tsl), 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#

4 papers in the tracked journals link to the GitHub repository TorchSpatiotemporal/tsl in their Crossref records, the first in 2025; most of them (2) in 2026.
By category: AI (3) and Operations Research (1); the journals with the most are Neural Networks (1), Transportation Research Part C Emerging Technologies (1) and Knowledge-Based Systems (1).

By year#

20252026

Papers linking to TorchSpatiotemporal/tsl by publication year
Year	Papers
2025	2
2026	2

Journals#

Journals whose papers link to TorchSpatiotemporal/tsl most
Journal	Papers
Neural Networks	1
Transportation Research Part C Emerging Technologies	1
Knowledge-Based Systems	1
Neural Computing and Applications	1

Papers linking to TorchSpatiotemporal/tsl, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-11-03	A systematic literature review of spatio-temporal graph neural network models for time series forecasting and classification	Neural Networks	Flavio Corradini, Flavio Gerosa, Marco Gori et al.	26	cites it
2025-07-21	CoSTI: Consistency models for (a faster) spatio-temporal imputation	Knowledge-Based Systems	Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno et al.	2	cites it
2026-07-08	Weaver: Kronecker product approximations of spatiotemporal attention for traffic network forecasting	Transportation Research Part C Emerging Technologies	Christopher Cheong, Gary A. Davis, Seongjin Choi	0	cites it
2026-10-01	CDGAT: A conditioned diffusion-based graph attention network for spatio-temporal prediction	Neural Computing and Applications	Angelo Casolaro, Massimiliano Giordano Orsini, Vincenzo Capone et al.	0	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). TorchSpatiotemporal/tsl: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-torchspatiotemporal-tsl

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

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