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Shenyang Huang

Preferred name: Andy
Ph.D. student at School of Computer Science, McGill University
and Mila - Quebec Artificial Intelligence Institute
E-mail: shenyang.huang@mail.mcgill.ca
Google Scholar: link
Github: https://github.com/shenyangHuang
CV: Shenyang Huang [updated 9.26.2023]
Linkedin: https://www.linkedin.com/in/shenyang-huang
Twitter: shenyangHuang

Bio

I am a final year Ph.D. student at Mila and McGill University , supervised by Professor Reihaneh Rabbany and Professor Guillaume Rabusseau . Previously I obtained an Honours in Computer Science from McGill University in 2019. I have a broad interest in temporal graph neural networks, graph transformers, graph neural networks and spectral methods. My research focuses on machine learning models for complex and evolving networks in the real world, referred to as Temporal Graph Learning (TGL). I also actively engage in building the TGL community by organizing the TGL reading group and two editions of the TGL workshop @ NeurIPS 2022 / 2023. Through my research, I aim to answer the following questions:

  • How to design more scalable, expressive and powerful temporal graph methods?
  • How to properly evaluate temporal graph methods based on real world considerations?
  • How to deploy TGL methods for applications such as disease modeling, anomaly detection and forecasting?

News!

  • [2024/02] Excited to announce our paper Temporal Graph Analysis with TGX is accepted at WSDM 2024 Demo Paper Track. Thanks to my amazing co-authors Razieh Shirzadkhani, Elahe Kooshafar, Reihaneh Rabbany and Farimah Poursafaei. TGX is a package that supports temporal graph loading, analysis and visualization and contains 19 TGX datasets from DGB and TGB out of the box. Stay tuned for upcoming talk on TGX.

  • [2024/01] See our blog post providing an overview of trends and future directions in Temporal Graph Learning so far in 2024. Thanks to my amazing co-authors: Emanuele Rossi, Michael Galkin, Andrea Cini and Ingo Scholtes.

  • [2024/01] Excited to annouce two papers accepted at ICLR 2024, Graphpulse: Topological representations for temporal graph property prediction and Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. Thanks to all my amazing collaborators!

  • [2023/12] Excited to announce our paper Understanding Opinions Towards Climate Change on Social Media is accepted at the NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning. Thanks my co-authors Yashaswi Pupneja, Joseph Zou and Sacha Lévy.

  • [2023/12] Invited Oral Talk at the LOG Meetup Mila Dec. 4th, presenting Temporal Graph Benchmark and Graphium. Here are the recordings (TGB: 1:49:41 and Graphium: 2:26:09) and slides.Thanks to the organizers for this great event!

  • [2023/11] Excited to annouce that our work "Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs" is accepted at TKDD. Thanks to my co-authors: Samy Coulombe, Yasmeen Hitti, Reihaneh Rabbany, Guillaume Rabusseau.

  • [2023/09] Excited to see our work "Temporal Graph Benchmark for Machine Learning on Temporal Graphs" accepted at NeurIPS 2023 Datasets and Benchmarks Track. See the updated arxiv version and TGB . See my talk at the temporal graph reading group on YouTube. Thanks to all my amazing coauthors: Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau and Reihaneh Rabbany.

  • Publications

    2024

  • Shamsi, K., Poursafaei, F., Huang, S., Ngo, B., Coskunuzer, B. and Akcora, C. Graphpulse: Topological representations for temporal graph property prediction (ICLR 2024)
  • Beaini, D., Huang, S., Cunha, J.A., Li, Z., Moisescu-Pareja, G., Dymov, O., Maddrell-Mander, S., McLean, C., Wenkel, F., Müller, L., Mohamud, J., Parviz, A., Craig, M., Koziarski, M., Lu, J., Zhu, Z., Gabellini, C., Klaser, K., Dean, J., Wognum, C., Sypetkowski, M., Rabusseau, G., Rabbany, R., Tang, J., Morris, C., Koutis, I., Ravanelli, M., Wolf, G., Tossou, P., Mary, H., Bois, T., Fitzgibbon, A., Banaszewski, B., Martin, C., and Masters, D. Towards Foundational Models For Molecular Learning on Large-scale Multi-task Datasets (ICLR 2024)
  • Shirzadkhani, R., Huang, S.,, Kooshafar, E., Rabbany, R., and Poursafaei, F. Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)

  • 2023

  • Huang, S.*,, Poursafaei, F.*, Danovitch, J., Fey, M., Hu, W., Rossi, E., Leskovec, J., Bronstein, M., Rabusseau G. and Rabbany R. Temporal Graph Benchmark for Machine Learning on Temporal Graphs (NeurIPS 2023 Datasets and Benchmarks Track)
  • Huang, S.,, Danovitch, J., Rabusseau G., Rabbany R. Fast and Attributed Change Detection on Dynamic Graphs with Density of States (PAKDD 2023)
  • Huang, S., Coulombe, S., Hitti, Y., Rabbany, R., Rabusseau, G. Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs (ACM Transactions on Knowledge Discovery from Data, TKDD)
  • Masters, D., Dean, J. Klaser, K., Li, Z., Mander, S., Sanders, A., Helal, H., Beker, D., Fitzgibbon, A., Huang, S., Rampášek, L., Beaini, D. GPS++: Reviving the Art of Message Passing for Molecular Property Prediction (TMLR)
  • Pupneja, Y., Zou, J., Lévy, S. Huang, S., Understanding Opinions Towards Climate Change on Social Media (NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning)
  • Jiang, L., Zhang, C., Poursafaei, F., Huang, S., Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations (preprint)

  • 2022

  • Poursafaei, F.*, Huang, S.*, Pelrine, K., Rabbany, R. Towards Better Evaluation for Dynamic Link Prediction (NeurIPS 2022 Datasets and Benchmarks Track)

  • 2021

  • Huang, S., Wang, K., Rabusseau, G., & Makhzani, A. Few Shot Image Generation via Implicit Autoencoding of Support Sets 5th Workshop on Meta-Learning at NeurIPS 2021
  • Huang, S., Rabusseau, G. & Rabbany, R. Scalable Change Point Detection for Dynamic Graphs 6th Outlier Detection and Description Workshop at KDD 2021
  • Huang, S., François-Lavet, V., & Rabusseau, G. Understanding Capacity Saturation in Incremental Learning. Canadian Conference on Artificial Intelligence 2021
  • Ding, X., Huang, S., Leung, A., Rabbany, R. Incorporating dynamic flight network in SEIR to model mobility between populations. Applied Network Science, Special issue on Epidemics Dynamics & Control on Networks

  • 2020

  • Huang, S., Hitti, Y., Rabusseau, G. & Rabbany, R. Laplacian Change Point Detection for Dynamic Graphs. (KDD 2020)
  • Leung, A., Ding, X., Huang, S., Rabbany, R. Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies.
  • Alletto, S., Huang, S., François-Lavet, V., Nakata, Y., & Rabusseau, G. RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. AAAI 2020 Meta-Eval Workshop

  • 2019

  • Huang, S., François-Lavet, V., & Rabusseau, G. Neural Architecture Search for Class-incremental Learning
    (previous version of "Understanding Capacity Saturation in Incremental Learning")

  • 2018

  • Huang, S., François-Lavet, V., Rabusseau, G. & Pineau, J. Exploring Continual Learning Using Incremental Architecture Search NeuIPS Continual Learning Workshop 2018.

  • Teaching

  • Winter 2023, Mentor, Representation Learning on Graphs and Networks L45, University of Cambridge
  • Fall 2022, Guest Lecturer, Anomaly Detection for Dynamic Graphs (updated slides), COMP 599 Network Science, McGill University
  • Fall 2021, Guest Lecturer, Anomaly Detection for Dynamic Graphs, COMP 599 Network Science, McGill University
  • Fall 2021, TA, COMP 599 Network Science, McGill University
  • Winter 2020, TA, COMP 250, Introduction to Computer Science, McGill University
  • Fall 2019, TA, COMP 202, Introduction to Programming, McGill University

  • Services

  • Organization Chair for Temporal Graph Learning Workshop@NeurIPS 2023
  • Organization Chair for Temporal Graph Learning Workshop@NeurIPS 2022
  • Organizer of the weekly Temporal Graph Reading Group
  • Organizer of the Temporal Graph Learning Community Slack, see here to join
  • NeurIPS 2023 Datasets and Benchmarks Track Reviewer
  • NeurIPS 2022 Datasets and Benchmarks Track Reviewer
  • Reviewer for Transactions on Machine Learning Research (TMLR) journal 2023
  • KDD 2021 External Reviewer
  • IEEE Transactions on Neural Networks and Learning Systems Reviewer 2021
  • ECML PKDD 2020 Program Committee Member
  • Awards and Scholarships

  • NSERC Postgraduate Scholarships-Doctoral (PGS D) Award, 2022-2025
  • Fonds de recherche du Québec – Nature et Technologies (FRQNT) Doctoral Award, 2022-2026
  • Scientist in Residence Program, Valence Labs and Mila, 2023
  • McGill Graduate Research Enhancement and Travel Awards (GREAT awards), 2023
  • Mitacs Accelerate Award, 2022
  • NSERC Undergraduate Student Research Awards, 2018
  • McGill Undergraduate Computer Science Research Award, 2nd Place Winner, 2018
  • McGill Physics Hackathon, 2nd Place Winner, 2017
  • NSERC Undergraduate Student Research Awards, 2016
  • Mentorship

  • 2022-2023, Research Assistant, Razieh Shirzadkhani, Disease Modeling with Dynamic Graphs & TGX package
  • 2023, Project Mentor, Lekang Jiang and Caiqi Zhang, Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
  • 2023, Project Mentor, Xiangjian Jiang and Yanyi Pu, Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks
  • 2022, Research Assistant, Abu bakar Daud, pypi package for Towards better dynamic link prediction