Temporal Graph Learning Workshop
The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data analysis.The workshop aims to facilitate the exchange of ideas across disciplines, highlight successes and challenges in TGL, and outline future research directions.The workshop welcomes diverse contributions, offers keynote talks from academic and industry experts, and is complemented by a panel discussion on emerging aspects of TGL.
Authors
- Michael M. Bronstein (ORCID: https://orcid.org/0000-0002-1262-7252)
- Farimah Poursafaei (ORCID: https://orcid.org/0009-0006-6785-3253)
- Julia Gastinger (ORCID: https://orcid.org/0000-0003-1914-6723)
- Reihaneh Rabbany (ORCID: https://orcid.org/0000-0003-2348-0353)
- Daniele Zambon (ORCID: https://orcid.org/0000-0003-3722-9784)
- Andrea Cini (ORCID: https://orcid.org/0000-0003-3219-9360)
- Jacob Chmura
- Shenyang Huang (ORCID: https://orcid.org/0000-0002-4526-7097)
Institutions
- University of Mannheim (DE)
- Science Oxford (GB)
- McGill University (CA)
- Università della Svizzera italiana (CH)
Publication Details
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1145/3711896.3737855
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Canadian Institute for Advanced Research
- McGill University
- DeepMind
- University of Oxford
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Universität Mannheim
- Natural Sciences and Engineering Research Council of Canada
- Engineering and Physical Sciences Research Council
- Fonds de recherche du Québec – Nature et technologies