TGL Workshop
@tgl_workshop
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Submission Deadline: May 25 AoE, 2025 (Openreview) Workshop: Aug 3 or 4, KDD 2025 (TBD) https://t.co/SUqfTtHvG3
Toronto
Joined August 2022
📅Workshop: Aug 3 or 4, KDD 2025, Toronto Canada (TBD).🔗 Workshop Website: 📒 If you'd like to review for the workshop, sign up here:.
docs.google.com
Thank you for your interest in reviewing research submissions for the Temporal Graph Learning Workshop@ KDD 2025. Please fill out the information in this form, so we can know more about your backgr...
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RT @tempgraph_rg: This week at the reading group, thursday, August 14th, 11am EDT (5pm CEST), @shenyangHuang will present: Are Large Langu….
arxiv.org
Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. While a broad range of literature has explored the...
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Join the Temporal Graph Learning Workshop at KDD 2025, we have an amazing program with great speakers and papers waiting for you. Let's shape together the future of temporal graph research. 📍Aug 4 | 1–5 PM | Room 714A, Toronto Convention Center.🔗
sites.google.com
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💡deadline extension for our Temporal Graph Learning workshop: new deadline is May 25th, AOE 💡. looking forward to your submissions!.
Submission deadline for our @kdd_news workshop is in 6 days, on May 20th AOE.😌. Topics include: Frontiers, Applications, Theory, Models, Methods and Evaluation for learning on temporal graphs!. Position papers, extended abstracts and standard papers. The venue is non-archival.
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RT @kdd_news: Applications for the KDD'25 Student Travel Award are now open🥁.🙌The awards are available to full-time students and offer part….
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Submission deadline for our @kdd_news workshop is in 6 days, on May 20th AOE.😌. Topics include: Frontiers, Applications, Theory, Models, Methods and Evaluation for learning on temporal graphs!. Position papers, extended abstracts and standard papers. The venue is non-archival.
📢 Announcing the third edition of Temporal Graph Learning Workshop at KDD 2025 📢.📜 Call for Papers: Submit your work on Temporal Graphs, Evolving Relational Data, Spatio-Temporal Graphs & more! .📅 Submission Deadline: May 20 AoE (Openreview).
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RT @kdd_news: Are you a PhD student interested in attending KDD-2025 in August? Submit your work in progress to KDD-2025 PhD Consortium! .T….
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🚨 15 days left! 🚨 .Don't miss the Call for Papers for the 3rd Temporal Graph Learning Workshop at KDD 2025 @kdd_news . 📅 Deadline: May 20 (OpenReview) .🔗
sites.google.com
Key dates
📢 Announcing the third edition of Temporal Graph Learning Workshop at KDD 2025 📢.📜 Call for Papers: Submit your work on Temporal Graphs, Evolving Relational Data, Spatio-Temporal Graphs & more! .📅 Submission Deadline: May 20 AoE (Openreview).
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🌟Call for Reviewers!🌟.We are currently recruiting reviewers for our upcoming workshop and would love to have your expertise on board! . Timeline: May 2025. 📝 Please sign up here: Thank you for supporting the workshop!.
docs.google.com
Thank you for your interest in reviewing research submissions for the Temporal Graph Learning Workshop@ KDD 2025. Please fill out the information in this form, so we can know more about your backgr...
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Organizing Team: @shenyangHuang, Farimah Poursafaei, Julia Gastinger, @dan_zambon, @andreacini1994, Jacob Chmura, @ReiRabb, @mmbronstein.
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We welcome full papers, position papers, and extended abstracts on Frontiers, Applications, Theory, Methods and Evaluation of Temporal Graphs. Find the full call for paper on our website. #KDD2025 #TemporalGraphs #MachineLearning.
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📢 Announcing the third edition of Temporal Graph Learning Workshop at KDD 2025 📢.📜 Call for Papers: Submit your work on Temporal Graphs, Evolving Relational Data, Spatio-Temporal Graphs & more! .📅 Submission Deadline: May 20 AoE (Openreview).
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RT @tempgraph_rg: 🌟New year, new Reading group session: 🌟. Join us on thursday, Jan 18th, 11am EST, where @alex_modell and Patrick Rubin-De….
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It was exciting to see so many researchers at our TGL workshop at @NeurIPSConf last Saturday, thanks for attending! Many thanks to our speakers and panelists for the great talks and panel discussion. Website: NeurIPS recording:
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🎺3 days to go 🎺.See you at our Temporal Graph Learning Workshop on saturday in room 203 - 205 or virtually @NeurIPSConf.#TemporalGraphs #NeurIPS23 #NeurIPS23
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check out this poster - here on twitter, or on saturday at our workshop 🙌 #NeurIPS2023.
On Saturday, I will present DURENDAL ⚔️ at the @tgl_workshop. If you are interested in snapshot-based temporal heterogeneous networks forecasting and two new benchmark datasets join me at my poster presentation! #NeurIPS2023
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Exciting Posters to come 🥳 #NeurIPS2023.
(3/3) Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting (Sat at @tgl_workshop) w/ Danilo Mandic and Cesare. TL;DR: We introduce HiGP, an end-to-end framework for graph-based hierarchical time series clustering and forecasting. 🔗
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5 days to go 🙌.Looking forward to our keynote speakers:.Nesreen Ahmed (9:30 am) @n_kahmed, Ingo Scholtes (10am) @ingo_S , Rex Ying (1:30pm) , Marinka Zitnik .(2pm) @marinkazitnik, and Kelsey Allen (2:30pm) @KelseyRAllen 💫.
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Further we spotlight:.SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks at 10:30 a.m. (.and.GenTKG: Generative Forecasting on Temporal Knowledge Graph at 10:45 a.m. (.✨✨.
openreview.net
The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional carefully designed embedding-based and rule-based...
6 days to go 🥳.Check out our spotlight talks:.Continuous-time Graph Representation with Sequential Survival Process at 8:30a.m (.followed by.Deep Graph Kernel Point Processes at 8:45a.m. (.Looking forward to Dec 16th!.
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6 days to go 🥳.Check out our spotlight talks:.Continuous-time Graph Representation with Sequential Survival Process at 8:30a.m (.followed by.Deep Graph Kernel Point Processes at 8:45a.m. (.Looking forward to Dec 16th!.
openreview.net
Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including...
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