Imagen de Portada

SA-GDA: Spectral Augmentation for Graph Domain Adaptation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Pang, Jinhui, Wang, Zixuan, Tang, Jiliang, Xiao, Mingyan, Yin, Nan
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

PyGDA: A Python Library for Graph Domain Adaptation
por: Zhang, Zhen, et al.
Publicado: (2025)

Degree-Conscious Spiking Graph for Cross-Domain Adaptation
por: Wang, Yingxu, et al.
Publicado: (2024)

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
por: Wang, Yingxu, et al.
Publicado: (2025)

DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential Equation
por: Wang, Yingxu, et al.
Publicado: (2024)

Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
por: Jian, Xiangru, et al.
Publicado: (2024)

Are Expressive Encoders Necessary for Discrete Graph Generation?
por: Revolinsky, Jay, et al.
Publicado: (2026)

Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
por: Shomer, Harry, et al.
Publicado: (2024)

Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
por: Liu, Meihan, et al.
Publicado: (2024)

Graph Domain Adaptation: Challenges, Progress and Prospects
por: Shi, Boshen, et al.
Publicado: (2024)

Rethinking Propagation for Unsupervised Graph Domain Adaptation
por: Liu, Meihan, et al.
Publicado: (2024)

On the Benefits of Attribute-Driven Graph Domain Adaptation
por: Fang, Ruiyi, et al.
Publicado: (2025)

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
por: Luo, Junyu, et al.
Publicado: (2024)

Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
por: Wang, Yilong, et al.
Publicado: (2025)

Graph Structure Learning with Bi-level Optimization
por: Yin, Nan
Publicado: (2024)

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
por: Yin, Nan, et al.
Publicado: (2023)

CoSA: Compressed Sensing-Based Adaptation of Large Language Models
por: Wei, Songtao, et al.
Publicado: (2026)

Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
por: Zhang, Zhen, et al.
Publicado: (2024)

Rank and Align: Towards Effective Source-free Graph Domain Adaptation
por: Luo, Junyu, et al.
Publicado: (2024)

DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
por: Wang, Yingxu, et al.
Publicado: (2026)

Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
por: Ma, Yao, et al.
Publicado: (2024)

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
por: Wang, Fan, et al.
Publicado: (2024)

Towards Neural Scaling Laws on Graphs
por: Liu, Jingzhe, et al.
Publicado: (2024)

Unveiling Mode Connectivity in Graph Neural Networks
por: Li, Bingheng, et al.
Publicado: (2025)

Learning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation
por: Chen, Wei, et al.
Publicado: (2026)

Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
por: Guo, Yihong, et al.
Publicado: (2024)

E-CGL: An Efficient Continual Graph Learner
por: Guo, Jianhao, et al.
Publicado: (2024)

Towards Understanding Link Predictor Generalizability Under Distribution Shifts
por: Revolinsky, Jay, et al.
Publicado: (2024)

Subgraph Generation for Generalizing on Out-of-Distribution Links
por: Revolinsky, Jay, et al.
Publicado: (2025)

Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
por: Chen, Wei, et al.
Publicado: (2024)

Uncovering Graph Reasoning in Decoder-only Transformers with Circuit Tracing
por: Dai, Xinnan, et al.
Publicado: (2025)

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
por: Chen, Zhikai, et al.
Publicado: (2023)

NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
por: Qing, Qing, et al.
Publicado: (2026)

Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
por: Chen, Wei, et al.
Publicado: (2026)

GAIN: Multiplicative Modulation for Domain Adaptation
por: Yao, Hengshuai, et al.
Publicado: (2026)

GCAL: Adapting Graph Models to Evolving Domain Shifts
por: Qiao, Ziyue, et al.
Publicado: (2025)

Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation
por: Leemann, Tobias, et al.
Publicado: (2024)

Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
por: Guo, Kai, et al.
Publicado: (2024)

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
por: Liu, Jingzhe, et al.
Publicado: (2024)

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
por: Dai, Xinnan, et al.
Publicado: (2024)

Higher-order Structure Boosts Link Prediction on Temporal Graphs
por: Liu, Jingzhe, et al.
Publicado: (2025)