DYGL: A Unified Benchmark and Library for Dynamic Graph. - Teng Ma; Bin Shi; Yiming Xu 001; Zihan Zhao; Siqi Liang; Bo Dong 001 (APWeb/WAIM (3), 2023)

CLDG: Contrastive Learning on Dynamic Graphs. - Yiming Xu 001; Bin Shi; Teng Ma; Bo Dong 001; Haoyi Zhou; Qinghua Zheng (ICDE, 2023)

APAM: Adaptive Pre-Training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-Tailed Learning. - Bo Dong 001; Yiming Xu 001; Sunyi Chi; Zhenyu Shi; Zheng Du (ICMLA, 2023)

An edge feature aware heterogeneous graph neural network model to support tax evasion detection. - Bin Shi; Bo Dong 001; Yiming Xu 001; Jiaxiang Wang; Yunfan Wang; Qinghua Zheng (Expert Syst. Appl., 2023)

Learning dynamic graph representations through timespan view contrasts. - Yiming Xu 001; Zhen Peng 005; Bin Shi; Xu Hua; Bo Dong 001 (Neural Networks, 2024)

Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective. - Yiming Xu 001; Zhen Peng 005; Bin Shi; Xu Hua; Bo Dong 001; Song Wang 013; Chen Chen 022 (AAAI, 2025)

Out-of-Distribution Generalization on Graphs via Progressive Inference. - Yiming Xu 001; Bin Shi; Zhen Peng 005; Huixiang Liu; Bo Dong 001; Chen Chen 022 (AAAI, 2025)

A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models. - Qika Lin; Zhen Peng 005; Kaize Shi; Kai He 001; Yiming Xu 001; Erik Cambria; Mengling Feng (CoRR, 2025)

TED: related party transaction guided tax evasion detection on heterogeneous graph. - Yiming Xu 001; Bin Shi; Bo Dong 001; Jiaxiang Wang; Hua Wei 001; Qinghua Zheng (Data Min. Knowl. Discov., 2025)