6 papers
Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
Shuai Zhang, Yancheng Chen, Chuan Zhou +5
Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis,…
Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
Yancheng Chen, Dun Ma, Shuai Zhang +6
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning h…
TransportBench: A Comprehensive Benchmark for Non-Equilibrium Flow Transport
Xu Wang, Minghao Li, Qizhen Hong +6
Scientific machine learning models, as versatile tools for numerical simulation and analysis, are increasingly transforming the landscape of fluid mechanics research. However, exis…
Beyond Pairwise Interactions: Equivariant Hypergraph Diffusion for Crystal Structure Prediction
Yang Liu, Chuan Zhou, Shuai Zhang +5
Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for materials discovery and the advancement of various scientific disciplines. Rece…
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9
Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performanc…
Hard Constraints Meet Soft Generation: Guaranteed Feasibility for LLM-based Combinatorial Optimization
Yang Liu, Chuan Zhou, Yancheng Chen +3
Large language models (LLMs) have emerged as promising general-purpose solvers for combinatorial optimization (CO), yet they fundamentally lack mechanisms to guarantee solution fea…