Image de couverture de livre

HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Hou, Yanheng, Li, Xunkai, Li, Zhenjun, Zhou, Bing, Li, Ronghua, Wang, Guoren
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Toward General and Robust LLM-enhanced Text-attributed Graph Learning
par: Zhang, Zihao, et autres
Publié: (2025)

RoleMAG: Learning Neighbor Roles in Multimodal Graphs
par: Zuo, Yilong, et autres
Publié: (2026)

ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
par: Wu, Xiang, et autres
Publié: (2025)

OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport
par: Zuo, Yilong, et autres
Publié: (2026)

DiRW: Path-Aware Digraph Learning for Heterophily
par: Su, Daohan, et autres
Publié: (2024)

Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
par: Fan, Bowen, et autres
Publié: (2025)

MagicDock: Toward Docking-oriented De Novo Ligand Design via Gradient Inversion
par: Chen, Zekai, et autres
Publié: (2025)

Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
par: Li, Xunkai, et autres
Publié: (2025)

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach
par: Zhang, Zhihan, et autres
Publié: (2025)

Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
par: Li, Xunkai, et autres
Publié: (2025)

AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
par: Li, Xunkai, et autres
Publié: (2024)

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
par: Sun, Henan, et autres
Publié: (2025)

Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
par: Su, Daohan, et autres
Publié: (2025)

Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
par: Liu, Sicheng, et autres
Publié: (2026)

Rethinking Client-oriented Federated Graph Learning
par: Chen, Zekai, et autres
Publié: (2025)

OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
par: Wan, Chenxi, et autres
Publié: (2026)

State Space Models over Directed Graphs
par: She, Junzhi, et autres
Publié: (2025)

STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
par: Chen, Zekai, et autres
Publié: (2026)

GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
par: Du, Enjun, et autres
Publié: (2025)

TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
par: Wu, Jiayi, et autres
Publié: (2026)

Federated Prototype Graph Learning
par: Wu, Zhengyu, et autres
Publié: (2025)

FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
par: Wu, Zhengyu, et autres
Publié: (2025)

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
par: Wang, Xu, et autres
Publié: (2026)

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
par: Wu, Xiang, et autres
Publié: (2026)

OpenGU: A Comprehensive Benchmark for Graph Unlearning
par: Fan, Bowen, et autres
Publié: (2025)

Towards Effective and General Graph Unlearning via Mutual Evolution
par: Li, Xunkai, et autres
Publié: (2024)

Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
par: Zhang, Sirui, et autres
Publié: (2026)

FedGTA: Topology-aware Averaging for Federated Graph Learning
par: Li, Xunkai, et autres
Publié: (2024)

Towards Data-centric Machine Learning on Directed Graphs: a Survey
par: Sun, Henan, et autres
Publié: (2024)

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
par: Li, Xunkai, et autres
Publié: (2024)

LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
par: Li, Xunkai, et autres
Publié: (2024)

Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
par: Li, Xunkai, et autres
Publié: (2025)

Rethinking Node-wise Propagation for Large-scale Graph Learning
par: Li, Xunkai, et autres
Publié: (2024)

DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
par: Chen, Zekai, et autres
Publié: (2026)

Unlocking Graph Structure Learning with Tree-Guided Large Language Models
par: Zhang, Zhihan, et autres
Publié: (2025)

BoostFGL: Boosting Fairness in Federated Graph Learning
par: Chen, Zekai, et autres
Publié: (2026)

Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
par: Sun, Henan, et autres
Publié: (2023)

Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
par: Li, Xunkai, et autres
Publié: (2025)

MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
par: Li, Xunkai, et autres
Publié: (2026)

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
par: Wen, Yanzhe, et autres
Publié: (2025)