| 2026 | AAAI | WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network. | Ruikun Li, Jiazhen Liu, Huandong Wang, Qingmin Liao, Yong Li |
| 2026 | WWW | Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching. | Shihe Zhou, Ruikun Li, Huandong Wang, Yong Li |
| 2025 | ICLR | Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning. | Ruikun Li, Huandong Wang, Qingmin Liao, Yong Li |
| 2025 | KDD | GDendrite: On Heterophilous Graph Contexts Mining with Versatile Neural Dendrites Framework. | Ruikun Li, Ye Xiao, Xiaoxiao Ma, Andrey Vasnev, Junbin Gao |
| 2025 | KDD | Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder. | Ruikun Li, Jingwen Cheng, Huandong Wang, Qingmin Liao, Yong Li |
| 2025 | PAKDD | On Leveraging Anomalies with Reference Alignment in Graph-Level Anomaly Detection. | Ye Xiao, Ruikun Li, Andrey Vasnev, Junbin Gao |
| 2024 | KDD | Graph Anomaly Detection with Few Labels: A Data-Centric Approach. | Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, Jia Wu |
| 2024 | KDD | Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space. | Ruikun Li, Huandong Wang, Jinghua Piao, Qingmin Liao, Yong Li |
| 2023 | ADMA | Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting. | Ruikun Li, Xuliang Li, Shiying Gao, S. T. Boris Choy, Junbin Gao |
| 2023 | IJCNN | Enhanced Loss Function based on Laplacian Eigenmaps for Graph Classification. | Ye Xiao, Ruikun Li, Andrey Vasnev, Junbin Gao |
| 2023 | KDD | Learning Slow and Fast System Dynamics via Automatic Separation of Time Scales. | Ruikun Li, Huandong Wang, Yong Li |