Copertina

MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shan, Lianze, Zhao, Jitao, He, Dongxiao, Huang, Yongqi, Feng, Zhiyong, Zhang, Weixiong
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
di: Shan, Lianze, et al.
Pubblicazione: (2026)

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
di: Li, Peiyuan, et al.
Pubblicazione: (2026)

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
di: He, Dongxiao, et al.
Pubblicazione: (2026)

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
di: Liang, Chundong, et al.
Pubblicazione: (2026)

One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
di: Huang, Yongqi, et al.
Pubblicazione: (2025)

Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
di: He, Dongxiao, et al.
Pubblicazione: (2025)

Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
di: Huang, Yongqi, et al.
Pubblicazione: (2025)

FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths
di: Mao, Xuqi, et al.
Pubblicazione: (2025)

HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
di: Liu, Ao, et al.
Pubblicazione: (2024)

Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
di: Yu, Jianxiang, et al.
Pubblicazione: (2022)

Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling
di: Li, Yujie, et al.
Pubblicazione: (2026)

Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs
di: Zou, Xingchen, et al.
Pubblicazione: (2024)

HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
di: Mo, Yujie, et al.
Pubblicazione: (2024)

Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
di: Zhou, Wei, et al.
Pubblicazione: (2026)

MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs
di: Chen, Jiawen, et al.
Pubblicazione: (2025)

Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
di: Wei, Xinxu, et al.
Pubblicazione: (2024)

Heterogeneous Low-Bandwidth Pre-Training of LLMs
di: Obeidi, Yazan, et al.
Pubblicazione: (2026)

Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
di: Hu, Shengchao, et al.
Pubblicazione: (2024)

GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs
di: Du, Enjun, et al.
Pubblicazione: (2025)

Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
di: Duan, Wenying, et al.
Pubblicazione: (2024)

A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders
di: Wei, Xinxu, et al.
Pubblicazione: (2025)

Non-Homophilic Graph Pre-Training and Prompt Learning
di: Yu, Xingtong, et al.
Pubblicazione: (2024)

Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
di: Liu, Yonghao, et al.
Pubblicazione: (2024)

Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
di: Liu, Yunhui, et al.
Pubblicazione: (2025)

Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
di: Wong, Zhen Hao, et al.
Pubblicazione: (2024)

MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
di: Zheng, Ziyu, et al.
Pubblicazione: (2026)

HERO: Heterogeneous Continual Graph Learning via Meta-Knowledge Distillation
di: Sun, Guiquan, et al.
Pubblicazione: (2025)

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
di: Ma, Yihong, et al.
Pubblicazione: (2023)

Echoless Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning
di: Hu, Jun, et al.
Pubblicazione: (2025)

Homophily-aware Heterogeneous Graph Contrastive Learning
di: Wang, Haosen, et al.
Pubblicazione: (2025)

Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs
di: Sun, Shengyin, et al.
Pubblicazione: (2025)

Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
di: Jin, Di, et al.
Pubblicazione: (2025)

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
di: He, Mingguo, et al.
Pubblicazione: (2023)

UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
di: Wang, Xiaotang, et al.
Pubblicazione: (2024)

Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
di: Wang, Xiaobao, et al.
Pubblicazione: (2025)

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
di: Wang, Yuhan, et al.
Pubblicazione: (2026)

Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
di: Du, Enjun, et al.
Pubblicazione: (2025)

Heterogeneous Graph Pre-training Based Model for Secure and Efficient Prediction of Default Risk Propagation among Bond Issuers
di: Li, Xurui, et al.
Pubblicazione: (2025)

Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective
di: Lu, Kangkang, et al.
Pubblicazione: (2024)

PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
di: Ge, Qingqing, et al.
Pubblicazione: (2023)