提出查询感知的可学习图池化标记作为大语言模型提示，增强图结构数据处理能力
arXiv:2501.17549v2 Announce Type: replace Abstract: Graph-structured data plays a vital role in numerous domains, such as social networks, citation ne…
基于动态线性度的忠诚可解释GNN方法，为图神经网络的复杂决策提供清晰可信的推理路径。
arXiv:2605.19778v1 Announce Type: new Abstract: We introduce B-cos GNNs, an inherently explainable class of graph neural networks whose predictions de…
提出针对稀疏文本属性图的高效可迁移预训练方法S2Aligner，解决LLM对齐中的监督不足问题。
arXiv:2605.18579v1 Announce Type: new Abstract: Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation mod…
Njord首次实现海洋概率预报，结合图神经网络与潜变量框架，支持全球/区域场景。
arXiv:2605.15470v1 Announce Type: new Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only determi…
用频谱分析揭示假新闻传播结构，为检测提供全新理论框架。
arXiv:2605.13861v1 Announce Type: cross Abstract: The propagation structure of fake news has been shown to be an important cue for detecting it; yet, …