12 citations · 36 across the 31 of their papers we have counts for
25 papers · 1 filter
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
Chunyu Hu, Tianyin Liao, Ge Lan +4
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been la…
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…
When Adaptation Fails: A Gradient-Based Diagnosis of Collapsed Gating in Vision-Language Prompt Learning
Yunxuan Fang, Ziwei Zhang, Xinhe Wang
Adaptive prompting mechanisms have been proposed to enhance vision-language models by dynamically tailoring prompts to inputs. However, in frozen few-shot prompt learning with CLIP…
TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models
Tianyin Liao, Chunyu Hu, Yicheng Sui +4
Link prediction is a fundamental task in graph machine learning with widespread applications such as recommendation systems, drug discovery, knowledge graphs, etc. In the foundatio…
Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models
Chuanyue Yu, Jiahui Wang, Yuhan Li +6
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (…
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
Zihao Guo, Qingyun Sun, Ziwei Zhang +4
Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL app…