temporal graph learning reading group
@tempgraph_rg
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🔸Reading Group for Research on Temporal Graph Learning 🔸Thursdays 11am-12pm ET 🔸 zoom 🔸 @shenyangHuang; Farimah Poursafaei; Julia Gastinger; @vstenby
Joined February 2023
You can now find us on BlueSky🦋: https://t.co/Sg0MWUpcks
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📢 This week at the Reading Group (Nov 13, 11am EST / 5pm CET), Jacob Chmura & @shenyangHuang present TGM: a Modular and Efficient Library for Machine Learning on Temporal Graph ⏰ zoom link on website!
Introducing TGM: Temporal Graph ML, Reimagined 🚀 The first open-source library unifying discrete & continuous-time GNNs under one API, built for speed, flexibility & research on temporal graphs. 🌟 https://t.co/eOAzImDdbs
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🪩This week at the reading group, thursday, november 6th, 11am EST (5pm CET), Emma Ceccherini (University of Bristol) will present: Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees 🪩 Looking forward to seeing you! zoom link on website.
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🗓️This weeks Reading Group: Thu, Oct 30 @ 11:00 AM EDT (note: 4:00 PM CET this week due to DST shift!) 👩🔬 Speaker: Edwige Cyffers (ISTA, Austria) 📄 Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy ML zoom link on website! 🥳
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This week at the reading group, thursday, Oct 23rd, 11am EDT, we are happy to have Andrea Ceni (University of Pisa), who will present "Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling". zoom link on website. 🥳
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This week at the reading group, thursday, oct 16th, 11am EDT, we are very happy to welcome @ManuelDileo, who will present Tensor Decomposition for Temporal Knowledge Graph Reasoning: From Completion to Forecasting. See you there 🥳🥳 zoom link on our website
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🍂This week at the reading group, thursday, Oct 9th, 11am EDT (5pm CEST), Zifeng Ding will present: Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning zoom link on website! 😊
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📚 The TGL reading group is hosting another session today, thursday oct 2nd 📚! We're excited to have Lu Yi from Renmin University of China on to discuss "Future Link Prediction Without Memory or Aggregation"! 🔗 Paper | https://t.co/T7TLsKTGCb Zoom link on the website
arxiv.org
Future link prediction on temporal graphs is a fundamental task with wide applicability in real-world dynamic systems. These scenarios often involve both recurring (seen) and novel (unseen)...
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This thursday, Sept 11th, 11am EDT (5pm CEST) we are happy to have Mathieu Chevalley (ETH Zurich and GSK), who will present: A large-scale benchmark for network inference from single-cell perturbation data https://t.co/U603stR93X zoom link on website 🥳
nature.com
Communications Biology - The authors introduce CausalBench, a benchmark suite that enhances network inference evaluation with real-world, large-scale single-cell perturbation data.
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TENET@CCS is here, join us in ROOM 15!🔥
📢 Speaker Alert for TENET@CCS 2025 (September 3 - morning): Davide Bacciu, Full Professor @ University of Pisa, Italy. Talk: "Temporal graph learning with dynamical systems" 📅 Submit your abstract by June 30: https://t.co/O0bwKUMgfb
#CCS2025 #Tenet25
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📚 This week, another reading group: 📆 Thursday, August 28th | 🕚 11am EDT, 5pm CEST 🎙️ Kiarash Shamsi, University of Manitoba presents MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs 🔗 Paper | https://t.co/Hkto7ctoXw 🧑💻 Zoom link on the webpage!
arxiv.org
Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on...
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📚 This week at the Temporal Graph Learning Reading Group! 🗓️ Thu, Aug 21 · 11am EDT / 5pm CEST 🎙️ Fang Wu & Vijay Prakash Dwivedi (Stanford) 📖 Large Language Models are Good Relational Learners 🔗 Paper: https://t.co/rO9xD3Z4Ys 💻 Zoom link on website Join us! 🎉
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This week at the reading group, thursday, August 14th, 11am EDT (5pm CEST), @shenyangHuang will present: Are Large Language Models Good Temporal Graph Learners? paper: https://t.co/sVEew60JeZ zoom link on website looking forward to seeing you! 🥳
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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Good news: The reading group starts again this thursday, july 24th, 11am EDT! We are happy to welcome Zijie Huang (Google DeepMind) to present: Neural Dynamics for Science: The Symbiosis of Deep Graph Learning and Differential Equations Meet you on zoom (link on website) 🎉🥳
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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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