RoGAT: a robust GNN combined revised GAT with adjusted graphs. - Xianchen Zhou; Hongxia Wang (CoRR, 2020)

The generalization error of graph convolutional networks may enlarge with more layers. - Xianchen Zhou; Hongxia Wang (Neurocomputing, 2021)

Progressive Perception Learning for Distribution Modulation in Siamese Tracking. - Kun Hu; Xianchen Zhou; Mingyu Cao; Mengzhu Wang; Guangjie Gao; Wenjing Yang 002; Huibin Tan (ICASSP, 2023)

Robustness meets accuracy in adversarial training for graph autoencoder. - Xianchen Zhou; Kun Hu; Hongxia Wang (Neural Networks, 2023)

On the Explainability of Graph Convolutional Network With GCN Tangent Kernel. - Xianchen Zhou; Hongxia Wang (Neural Comput., 2023)

Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking. - Kun Hu; Wenjing Yang 002; Wanrong Huang; Xianchen Zhou; Mingyu Cao; Jing Ren; Huibin Tan (AAAI, 2024)

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images. - Shuchang Zhang; Ziyang Yuan; Xianchen Zhou; Hongxia Wang; Bo Chen; Yadong Wang (Comput. Methods Programs Biomed., 2024)

A robust graph attention network with dynamic adjusted graph. - Xianchen Zhou; Yaoyun Zeng; Zepeng Hao; Hongxia Wang (Eng. Appl. Artif. Intell., 2024)

L2M-GCN: A new framework for learning robust GCN against structural attacks. - Haoran Chen; Xianchen Zhou; Jiwei Zhang 002; Hongxia Wang (Neurocomputing, 2025)