Cover Image

HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mo, Yujie, Yu, Runpeng, Zhu, Xiaofeng, Wang, Xinchao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
by: Mo, Yujie, et al.
Published: (2024)

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
by: Xia, Yutong, et al.
Published: (2024)

KAN or MLP: A Fairer Comparison
by: Yu, Runpeng, et al.
Published: (2024)

Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior
by: Yin, Bo, et al.
Published: (2026)

Discrete Diffusion in Large Language and Multimodal Models: A Survey
by: Yu, Runpeng, et al.
Published: (2025)

Multi-Level Collaboration in Model Merging
by: Li, Qi, et al.
Published: (2025)

Introducing Visual Perception Token into Multimodal Large Language Model
by: Yu, Runpeng, et al.
Published: (2025)

Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models
by: Chen, Yuyan, et al.
Published: (2024)

Training Neural Networks from Scratch with Parallel Low-Rank Adapters
by: Huh, Minyoung, et al.
Published: (2024)

PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters
by: Thapa, Krishu K, et al.
Published: (2025)

Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
by: Huang, Jincheng, et al.
Published: (2024)

HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection
by: Yang, Guang, et al.
Published: (2025)

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
by: Kim, Bum Jun, et al.
Published: (2026)

Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
by: Boudier, Luc, et al.
Published: (2025)

Adapter-Based Multi-Agent AVSR Extension for Pre-Trained ASR Models
by: Simic, Christopher, et al.
Published: (2025)

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
by: Jin, Dian, et al.
Published: (2025)

Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation
by: Li, Yi-Chen, et al.
Published: (2024)

Training-Only Heterogeneous Image-Patch-Text Graph Supervision for Advancing Few-Shot Learning Adapters
by: Mohammad, Mohammed Rahman Sherif Khan, et al.
Published: (2026)

Integrating Task-Specific and Universal Adapters for Pre-Trained Model-based Class-Incremental Learning
by: Wang, Yan, et al.
Published: (2025)

Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
by: Bhardwaj, Kartikeya, et al.
Published: (2024)

Dual-Personalizing Adapter for Federated Foundation Models
by: Yang, Yiyuan, et al.
Published: (2024)

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
by: Roschkowski, Marco
Published: (2025)

Enhancing Signed Graph Neural Networks through Curriculum-Based Training
by: Zhang, Zeyu, et al.
Published: (2023)

Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning
by: Zhou, Sashuai, et al.
Published: (2025)

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
by: Wang, Huiyi, et al.
Published: (2024)

Robustness Feature Adapter for Efficient Adversarial Training
by: Wu, Quanwei, et al.
Published: (2025)

Asymmetry in Low-Rank Adapters of Foundation Models
by: Zhu, Jiacheng, et al.
Published: (2024)

Diving into Kronecker Adapters: Component Design Matters
by: Bai, Jiayu, et al.
Published: (2026)

Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency
by: Nguyen, Duy Phuong, et al.
Published: (2025)

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
by: Yu, Wneya, et al.
Published: (2026)

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

Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
by: Shi, Haizhou, et al.
Published: (2024)

Transcoder Adapters for Reasoning-Model Diffing
by: Hu, Nathan, et al.
Published: (2026)

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

Adapters Strike Back
by: Steitz, Jan-Martin O., et al.
Published: (2024)

Sparse High Rank Adapters
by: Bhardwaj, Kartikeya, et al.
Published: (2024)

MoKA: Mixture of Kronecker Adapters
by: Sadeghi, Mohammadreza, et al.
Published: (2025)

Lethe:Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
by: Tan, Hanwei, et al.
Published: (2026)

Block Circulant Adapter for Large Language Models
by: Ding, Xinyu, et al.
Published: (2025)

Neural Lineage
by: Yu, Runpeng, et al.
Published: (2024)