3 papers
cs.LG2026
Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
Baoheng Zhu, Deyu Bo, Delvin Ce Zhang +1
Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has e…
cs.LG2026
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
Yuhan Wang, Haopeng Zhang, Yibo Ding +6
Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through…
cs.LG2025
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Zhibiao Wang, Yunlong Zhou, Ziwei Zhang +4
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive…