Guardado en:
| Autores principales: | Fan, Bowen, Guo, Zhilin, Li, Xunkai, Zhou, Yihan, Zhou, Bing, Li, Zhenjun, Li, Rong-Hua, Wang, Guoren |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.12233 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Toward General and Robust LLM-enhanced Text-attributed Graph Learning
por: Zhang, Zihao, et al.
Publicado: (2025)
por: Zhang, Zihao, et al.
Publicado: (2025)
OpenGU: A Comprehensive Benchmark for Graph Unlearning
por: Fan, Bowen, et al.
Publicado: (2025)
por: Fan, Bowen, et al.
Publicado: (2025)
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
por: Zhang, Zhihan, et al.
Publicado: (2025)
por: Zhang, Zhihan, et al.
Publicado: (2025)
Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
por: Li, Xunkai, et al.
Publicado: (2025)
por: Li, Xunkai, et al.
Publicado: (2025)
HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization
por: Hou, Yanheng, et al.
Publicado: (2025)
por: Hou, Yanheng, et al.
Publicado: (2025)
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2026)
por: Chen, Zekai, et al.
Publicado: (2026)
MagicDock: Toward Docking-oriented De Novo Ligand Design via Gradient Inversion
por: Chen, Zekai, et al.
Publicado: (2025)
por: Chen, Zekai, et al.
Publicado: (2025)
DiRW: Path-Aware Digraph Learning for Heterophily
por: Su, Daohan, et al.
Publicado: (2024)
por: Su, Daohan, et al.
Publicado: (2024)
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
por: Li, Xunkai, et al.
Publicado: (2025)
por: Li, Xunkai, et al.
Publicado: (2025)
State Space Models over Directed Graphs
por: She, Junzhi, et al.
Publicado: (2025)
por: She, Junzhi, et al.
Publicado: (2025)
Federated Graph Unlearning
por: Ai, Yuming, et al.
Publicado: (2025)
por: Ai, Yuming, et al.
Publicado: (2025)
Rethinking Client-oriented Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2025)
por: Chen, Zekai, et al.
Publicado: (2025)
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
por: Wu, Xiang, et al.
Publicado: (2025)
por: Wu, Xiang, et al.
Publicado: (2025)
Federated Prototype Graph Learning
por: Wu, Zhengyu, et al.
Publicado: (2025)
por: Wu, Zhengyu, et al.
Publicado: (2025)
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
por: Du, Enjun, et al.
Publicado: (2025)
por: Du, Enjun, et al.
Publicado: (2025)
FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
por: Wu, Zhengyu, et al.
Publicado: (2025)
por: Wu, Zhengyu, et al.
Publicado: (2025)
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
por: Wu, Zhengyu, et al.
Publicado: (2025)
por: Wu, Zhengyu, et al.
Publicado: (2025)
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
por: Wang, Xu, et al.
Publicado: (2026)
por: Wang, Xu, et al.
Publicado: (2026)
Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
por: Wu, Xiang, et al.
Publicado: (2026)
por: Wu, Xiang, et al.
Publicado: (2026)
Unlocking Graph Structure Learning with Tree-Guided Large Language Models
por: Zhang, Zhihan, et al.
Publicado: (2025)
por: Zhang, Zhihan, et al.
Publicado: (2025)
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
por: Su, Daohan, et al.
Publicado: (2025)
por: Su, Daohan, et al.
Publicado: (2025)
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
por: Wu, Jiayi, et al.
Publicado: (2026)
por: Wu, Jiayi, et al.
Publicado: (2026)
MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
por: Li, Xunkai, et al.
Publicado: (2026)
por: Li, Xunkai, et al.
Publicado: (2026)
Towards Effective and General Graph Unlearning via Mutual Evolution
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
FedGTA: Topology-aware Averaging for Federated Graph Learning
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
Towards Data-centric Machine Learning on Directed Graphs: a Survey
por: Sun, Henan, et al.
Publicado: (2024)
por: Sun, Henan, et al.
Publicado: (2024)
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
por: Wen, Yanzhe, et al.
Publicado: (2025)
por: Wen, Yanzhe, et al.
Publicado: (2025)
DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
por: Chen, Zekai, et al.
Publicado: (2026)
por: Chen, Zekai, et al.
Publicado: (2026)
RoleMAG: Learning Neighbor Roles in Multimodal Graphs
por: Zuo, Yilong, et al.
Publicado: (2026)
por: Zuo, Yilong, et al.
Publicado: (2026)
OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
por: Wan, Chenxi, et al.
Publicado: (2026)
por: Wan, Chenxi, et al.
Publicado: (2026)
Rethinking Node-wise Propagation for Large-scale Graph Learning
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
por: Zhang, Sirui, et al.
Publicado: (2026)
por: Zhang, Sirui, et al.
Publicado: (2026)
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
por: Li, Xunkai, et al.
Publicado: (2025)
por: Li, Xunkai, et al.
Publicado: (2025)
BoostFGL: Boosting Fairness in Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2026)
por: Chen, Zekai, et al.
Publicado: (2026)
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
por: Sun, Henan, et al.
Publicado: (2023)
por: Sun, Henan, et al.
Publicado: (2023)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
por: Wu, Zhengyu, et al.
Publicado: (2025)
por: Wu, Zhengyu, et al.
Publicado: (2025)
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
por: Li, Xunkai, et al.
Publicado: (2024)
por: Li, Xunkai, et al.
Publicado: (2024)
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
por: Sun, Henan, et al.
Publicado: (2025)
por: Sun, Henan, et al.
Publicado: (2025)
Ejemplares similares
-
Toward General and Robust LLM-enhanced Text-attributed Graph Learning
por: Zhang, Zihao, et al.
Publicado: (2025) -
OpenGU: A Comprehensive Benchmark for Graph Unlearning
por: Fan, Bowen, et al.
Publicado: (2025) -
When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
por: Zhang, Zhihan, et al.
Publicado: (2025) -
Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
por: Li, Xunkai, et al.
Publicado: (2025) -
HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization
por: Hou, Yanheng, et al.
Publicado: (2025)