5 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
Yanzhe Wen, Xunkai Li, Qi Zhang +4
Recently, large language models (LLMs) have significantly advanced text-attributed graph (TAG) learning. However, existing methods inadequately handle data uncertainty in open-worl…
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
Henan Sun, Xunkai Li, Lei Zhu +4
Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation unde…
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
Xunkai Li, Zhengyu Wu, Kaichi Yu +4
The directed graph (digraph), as a generalization of undirected graphs, exhibits superior representation capability in modeling complex topology systems and has garnered considerab…
LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models
Zhihan Zhang, Xunkai Li, Lei Zhu +6
The emergence of large language models (LLMs) has popularized text-attributed graphs (TAGs), creating an urgent need for graph structure learning (GSL) methods that effectively lev…
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
Xunkai Li, Daohan Su, Zhengyu Wu +4
The -parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node featu…
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
Zhengyu Wu, Guang Zeng, Huilin Lai +7
Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preservi…