Neural Architecture Search for Class-incremental Learning. - Shenyang Huang; Vincent François-Lavet; Guillaume Rabusseau (CoRR, 2019)

Laplacian Change Point Detection for Dynamic Graphs. - Shenyang Huang; Yasmeen Hitti; Guillaume Rabusseau; Reihaneh Rabbany (KDD, 2020)

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. - Stefano Alletto; Shenyang Huang; Vincent François-Lavet; Yohei Nakata; Guillaume Rabusseau (CoRR, 2020)

Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies. - Abby Leung; Xiaoye Ding; Shenyang Huang; Reihaneh Rabbany (CoRR, 2020)

Understanding Capacity Saturation in Incremental Learning. - Shenyang Huang; Vincent François-Lavet; Guillaume Rabusseau (Canadian AI, 2021)

Incorporating dynamic flight network in SEIR to model mobility between populations. - Xiaoye Ding; Shenyang Huang; Abby Leung; Reihaneh Rabbany (Appl. Netw. Sci., 2021)

Towards Better Evaluation for Dynamic Link Prediction. - Farimah Poursafaei; Shenyang Huang; Kellin Pelrine; Reihaneh Rabbany (NeurIPS, 2022)

Temporal Graph Benchmark for Machine Learning on Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Jacob Danovitch; Matthias Fey; Weihua Hu; Emanuele Rossi; Jure Leskovec; Michael M. Bronstein; Guillaume Rabusseau; Reihaneh Rabbany (NeurIPS, 2023)

Fast and Attributed Change Detection on Dynamic Graphs with Density of States. - Shenyang Huang; Jacob Danovitch; Guillaume Rabusseau; Reihaneh Rabbany (PAKDD (1), 2023)

Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations. - Lekang Jiang; Caiqi Zhang; Farimah Poursafaei; Shenyang Huang (CoRR, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (CoRR, 2023)

Understanding Opinions Towards Climate Change on Social Media. - Yashaswi Pupneja; Joseph Zou; Sacha Lévy; Shenyang Huang (CoRR, 2023)

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Kläser 001; Zhiyi Li; Samuel Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Andrew W. Fitzgibbon; Shenyang Huang; Ladislav Rampásek; Dominique Beaini (Trans. Mach. Learn. Res., 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (ICLR, 2024)

GraphPulse: Topological representations for temporal graph property prediction. - Kiarash Shamsi; Farimah Poursafaei; Shenyang Huang; Tran Gia Bao Ngo; Baris Coskunuzer; Cuneyt Gurcan Akcora (ICLR, 2024)

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Reihaneh Rabbany; Guillaume Rabusseau; Emanuele Rossi (LoG, 2024)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. - Julia Gastinger; Shenyang Huang; Michael Galkin; Erfan Loghmani; Ali Parviz; Farimah Poursafaei; Jacob Danovitch; Emanuele Rossi; Ioannis Koutis; Heiner Stuckenschmidt; Reihaneh Rabbany; Guillaume Rabusseau (NeurIPS, 2024)

Temporal Graph Analysis with TGX. - Razieh Shirzadkhani; Shenyang Huang; Elahe Kooshafar; Reihaneh Rabbany; Farimah Poursafaei (WSDM, 2024)

MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning. - Kerstin Kläser 001; Blazej Banaszewski; Samuel Maddrell-Mander; Callum McLean; Luis Müller; Ali Parviz; Shenyang Huang; Andrew W. Fitzgibbon (CoRR, 2024)

Temporal Graph Rewiring with Expander Graphs. - Katarina Petrovic; Shenyang Huang; Farimah Poursafaei; Petar Velickovic (CoRR, 2024)

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs. - Razieh Shirzadkhani; Tran Gia Bao Ngo; Kiarash Shamsi; Shenyang Huang; Farimah Poursafaei; Poupak Azad; Reihaneh Rabbany; Baris Coskunuzer; Guillaume Rabusseau; Cuneyt Gurcan Akcora (CoRR, 2024)

ContextGNN: Beyond Two-Tower Recommendation Systems. - Yiwen Yuan; Zecheng Zhang; Xinwei He; Akihiro Nitta; Weihua Hu; Dong Wang; Manan Shah; Shenyang Huang; Blaz Stojanovic; Alan Krumholz; Jan Eric Lenssen; Jure Leskovec; et al. (CoRR, 2024)

Differential Mnemonic Contributions of Cortical Representations during Encoding and Retrieval. - Cortney M. Howard; Shenyang Huang; Mariam Hovhannisyan; Roberto Cabeza; Simon W. Davis (J. Cogn. Neurosci., 2024)

Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs. - Shenyang Huang; Samy Coulombe; Yasmeen Hitti; Reihaneh Rabbany; Guillaume Rabusseau (ACM Trans. Knowl. Discov. Data, 2024)

ContextGNN: Beyond Two-Tower Recommendation Systems. - Yiwen Yuan; Zecheng Zhang; Xinwei He; Akihiro Nitta; Weihua Hu; Manan Shah; Blaz Stojanovic; Shenyang Huang; Jan Eric Lenssen; Jure Leskovec; Matthias Fey (ICLR, 2025)

Subsequent Memory Effects in Cortical Pattern Similarity Differ by Semantic Class. - Christina Yu; Shenyang Huang; Cortney M. Howard; Mariam Hovhannisyan; Alex Clarke; Roberto Cabeza; Simon W. Davis (J. Cogn. Neurosci., 2025)

Are Large Language Models Good Temporal Graph Learners? - Shenyang Huang; Ali Parviz; Emma Kondrup; Zachary Yang; Zifeng Ding; Michael M. Bronstein; Reihaneh Rabbany; Guillaume Rabusseau (CoRR, 2025)

Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures. - Vijay Prakash Dwivedi; Charilaos I. Kanatsoulis; Shenyang Huang; Jure Leskovec (CoRR, 2025)

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs. - Alireza Dizaji; Benedict Aaron Tjandra; Mehrab Hamidi; Shenyang Huang; Guillaume Rabusseau (CoRR, 2025)