When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
Fuente:
arXiv
Saved in:
| Main Authors: | Wen, Yanzhe, Li, Xunkai, Zhang, Qi, Lei, Zhu, Zeng, Guang, Li, Rong-Hua, Wang, Guoren |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
State Space Models over Directed Graphs
by: She, Junzhi, et al.
Published: (2025)
by: She, Junzhi, et al.
Published: (2025)
Unlocking Graph Structure Learning with Tree-Guided Large Language Models
by: Zhang, Zhihan, et al.
Published: (2025)
by: Zhang, Zhihan, et al.
Published: (2025)
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
by: Li, Xunkai, et al.
Published: (2025)
by: Li, Xunkai, et al.
Published: (2025)
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
by: Li, Xunkai, et al.
Published: (2025)
by: Li, Xunkai, et al.
Published: (2025)
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Federated Prototype Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
by: Li, Xunkai, et al.
Published: (2026)
by: Li, Xunkai, et al.
Published: (2026)
DiRW: Path-Aware Digraph Learning for Heterophily
by: Su, Daohan, et al.
Published: (2024)
by: Su, Daohan, et al.
Published: (2024)
OpenGU: A Comprehensive Benchmark for Graph Unlearning
by: Fan, Bowen, et al.
Published: (2025)
by: Fan, Bowen, et al.
Published: (2025)
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
by: Li, Xunkai, et al.
Published: (2025)
by: Li, Xunkai, et al.
Published: (2025)
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
by: Sun, Henan, et al.
Published: (2023)
by: Sun, Henan, et al.
Published: (2023)
Towards Data-centric Machine Learning on Directed Graphs: a Survey
by: Sun, Henan, et al.
Published: (2024)
by: Sun, Henan, et al.
Published: (2024)
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Rethinking Node-wise Propagation for Large-scale Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
by: Su, Daohan, et al.
Published: (2026)
by: Su, Daohan, et al.
Published: (2026)
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
by: Zhang, Sirui, et al.
Published: (2026)
by: Zhang, Sirui, et al.
Published: (2026)
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
by: Wang, Xu, et al.
Published: (2026)
by: Wang, Xu, et al.
Published: (2026)
Rethinking Client-oriented Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2025)
by: Chen, Zekai, et al.
Published: (2025)
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
by: Sun, Henan, et al.
Published: (2025)
by: Sun, Henan, et al.
Published: (2025)
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
by: Wu, Jiayi, et al.
Published: (2026)
by: Wu, Jiayi, et al.
Published: (2026)
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
by: Wu, Xiang, et al.
Published: (2025)
by: Wu, Xiang, et al.
Published: (2025)
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
by: Su, Daohan, et al.
Published: (2025)
by: Su, Daohan, et al.
Published: (2025)
A Topology-Aware Positive Sample Set Construction and Feature Optimization Method in Implicit Collaborative Filtering
by: Wu, Jiayi, et al.
Published: (2026)
by: Wu, Jiayi, et al.
Published: (2026)
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
by: Wu, Jiayi, et al.
Published: (2026)
by: Wu, Jiayi, et al.
Published: (2026)
DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis
by: Zhang, Ru, et al.
Published: (2026)
by: Zhang, Ru, et al.
Published: (2026)
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
by: Zhang, Zhihan, et al.
Published: (2025)
by: Zhang, Zhihan, et al.
Published: (2025)
A Comprehensive Data-centric Overview of Federated Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
by: Fan, Bowen, et al.
Published: (2025)
by: Fan, Bowen, et al.
Published: (2025)
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
by: Zhu, Yinlin, et al.
Published: (2024)
by: Zhu, Yinlin, et al.
Published: (2024)
Toward General and Robust LLM-enhanced Text-attributed Graph Learning
by: Zhang, Zihao, et al.
Published: (2025)
by: Zhang, Zihao, et al.
Published: (2025)
Large Language-Geometry Model: When LLM meets Equivariance
by: Li, Zongzhao, et al.
Published: (2025)
by: Li, Zongzhao, et al.
Published: (2025)
Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
by: Liu, Sicheng, et al.
Published: (2026)
by: Liu, Sicheng, et al.
Published: (2026)
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
by: Wu, Xiang, et al.
Published: (2026)
by: Wu, Xiang, et al.
Published: (2026)
Towards Effective and General Graph Unlearning via Mutual Evolution
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Similar Items
-
State Space Models over Directed Graphs
by: She, Junzhi, et al.
Published: (2025) -
Unlocking Graph Structure Learning with Tree-Guided Large Language Models
by: Zhang, Zhihan, et al.
Published: (2025) -
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
by: Li, Xunkai, et al.
Published: (2025) -
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
by: Li, Xunkai, et al.
Published: (2025) -
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
by: Li, Xunkai, et al.
Published: (2024)