5 papers
Converse and Collision-Based Achievability for Node Localization with Hybrid Distance-Spectral Graph Positional Encodings
Zimo Yan, Yifan Li, Hao Li +4
Graph positional encodings are widely used in graph neural networks and graph Transformers, yet it remains unclear when the code itself can identify nodes. We study a hybrid distan…
Information-Theoretic Limits of Node Localization under Hybrid Graph Positional Encodings
Zimo Yan, Zheng Xie, Chang Liu +2
Positional encoding has become a standard component in graph learning, especially for graph Transformers and other models that must distinguish structurally similar nodes, yet its…
Bridging Distance and Spectral Positional Encodings via Anchor-Based Diffusion Geometry Approximation
Zimo Yan, Zheng Xie, Runfan Duan +2
Molecular graph learning benefits from positional signals that capture both local neighborhoods and global topology. Two widely used families are spectral encodings derived from La…
Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodings
Zimo Yan, Zheng Xie, Chang Liu +1
Message passing graph neural networks are widely used for learning on graphs, yet their expressive power is limited by the one-dimensional Weisfeiler-Lehman test and can fail to di…
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
Zimo Yan, Jie Zhang, Zheng Xie +3
Molecular generation plays an important role in drug discovery and materials science, especially in data-scarce scenarios where traditional generative models often struggle to achi…