Zhaoliang Chen

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing

2023 · arXiv (Cornell University)

Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which …

Dual Low-Rank Graph Autoencoder for Semantic and Topological Networks

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Due to the powerful capability to gather the information of neighborhood nodes, Graph Convolutional Network (GCN) has become a widely explored hotspot in recent years. As a well-established extension, Graph AutoEncoder (GAE) succeeds in mining …

ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

2024 · arXiv (Cornell University)

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by …

Towards Multi-view Consistent Graph Diffusion

2024

Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for modeling both graph structures and features, making them a …