Graph Denoising with Framelet Regularizer. - Bingxin Zhou; Ruikun Li 001; Xuebin Zheng; Yu Guang Wang 001; Junbin Gao (CoRR, 2021)

Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting. - Ruikun Li 001; Xuliang Li 009; Shiying Gao; S. T. Boris Choy; Junbin Gao (ADMA (1), 2023)

Enhanced Loss Function based on Laplacian Eigenmaps for Graph Classification. - Ye Xiao; Ruikun Li 001; Andrey Vasnev; Junbin Gao (IJCNN, 2023)

Graph structure learning layer and its graph convolution clustering application. - Xiaxia He; Boyue Wang; Ruikun Li 001; Junbin Gao; Yongli Hu; Guangyu Huo; Baocai Yin (Neural Networks, 2023)

Graph Anomaly Detection with Few Labels: A Data-Centric Approach. - Xiaoxiao Ma 002; Ruikun Li 001; Fanzhen Liu; Kaize Ding; Jian Yang 001; Jia Wu 001 (KDD, 2024)

Diffusion models for time-series applications: a survey. - Lequan Lin; Zhengkun Li; Ruikun Li 001; Xuliang Li 009; Junbin Gao (Frontiers Inf. Technol. Electron. Eng., 2024)

Graph Denoising With Framelet Regularizers. - Bingxin Zhou; Ruikun Li 001; Xuebin Zheng; Yu Guang Wang 001; Junbin Gao (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

On Leveraging Anomalies with Reference Alignment in Graph-Level Anomaly Detection. - Ye Xiao; Ruikun Li 001; Andrey Vasnev; Junbin Gao (PAKDD (1), 2025)