33 citations · 47 across the 21 of their papers we have counts for
10 papers · 1 filter
Relation-Aware Graph Foundation Model
Jianxiang Yu, Jiapeng Zhu, Hao Qian +3
In recent years, large language models (LLMs) have demonstrated remarkable generalization capabilities across various natural language processing (NLP) tasks. Similarly, graph foun…
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Long Zeng, Jianxiang Yu, Jiapeng Zhu +2
Graph self-supervised learning has gained significant attention recently. However, many existing approaches heavily depend on perturbations, and inappropriate perturbations may cor…
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
Jiapeng Zhu, Zichen Ding, Jianxiang Yu +3
The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in…
Boosting Graph Foundation Model from Structural Perspective
Yao Cheng, Yige Zhao, Jianxiang Yu +1
Graph foundation models have recently attracted significant attention due to its strong generalizability. Although existing methods resort to language models to learn unified seman…
Improving Graph Out-of-distribution Generalization Beyond Causality
Can Xu, Yao Cheng, Jianxiang Yu +4
Existing methods for graph out-of-distribution (OOD) generalization primarily rely on empirical studies on synthetic datasets. Such approaches tend to overemphasize the causal rela…
Resist Label Noise with PGM for Graph Neural Networks
Qingqing Ge, Jianxiang Yu, Zeyuan Zhao +1
While robust graph neural networks (GNNs) have been widely studied for graph perturbation and attack, those for label noise have received significantly less attention. Most existin…