 DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal ForecastingKnowledge Discovery and Data Mining (KDD), 2024 |
 Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph
Neural NetworksAAAI Conference on Artificial Intelligence (AAAI), 2024 |
 The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for
Heterophilic GraphsKnowledge Discovery and Data Mining (KDD), 2024 |
 Interpretable Sparsification of Brain Graphs: Better Practices and
Effective Designs for Graph Neural NetworksKnowledge Discovery and Data Mining (KDD), 2023 |
 Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural
NetworksInternational Conference on Learning Representations (ICLR), 2023 |
 Learning to Drop: Robust Graph Neural Network via Topological DenoisingWeb Search and Data Mining (WSDM), 2020 |
 Inferring Degrees from Incomplete Networks and Nonlinear DynamicsInternational Joint Conference on Artificial Intelligence (IJCAI), 2020 |
