Empowering GNNs for Domain Adaptation via Denoising Target Graph

• URL: http://arxiv.org/abs/2512.06236v2
• Date: Thu, 11 Dec 2025 21:07:56 GMT
• Title: Empowering GNNs for Domain Adaptation via Denoising Target Graph
• Authors: Haiyang Yu, Meng-Chieh Lee, Xiang song, Qi Zhu, Christos Faloutsos,
• Abstract summary: We explore the node classification task in the context of graph domain adaptation, which uses both source and target graph structures along with source labels to enhance the generalization capabilities of Graph Neural Networks (GNNs) on target graphs.<n>We find that simply incorporating an auxiliary loss function for denoising graph edges on target graphs can be extremely effective in enhancing GNN performance on target graphs.<n>We propose our framework, GraphDeT, a framework that integrates this auxiliary edge task into GNN training for node classification under domain adaptation.
• Score: 24.1681316493239
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We explore the node classification task in the context of graph domain adaptation, which uses both source and target graph structures along with source labels to enhance the generalization capabilities of Graph Neural Networks (GNNs) on target graphs. Structure domain shifts frequently occur, especially when graph data are collected at different times or from varying areas, resulting in poor performance of GNNs on target graphs. Surprisingly, we find that simply incorporating an auxiliary loss function for denoising graph edges on target graphs can be extremely effective in enhancing GNN performance on target graphs. Based on this insight, we propose our framework, GraphDeT, a framework that integrates this auxiliary edge task into GNN training for node classification under domain adaptation. Our theoretical analysis connects this auxiliary edge task to the graph generalization bound with -distance, demonstrating such auxiliary task can imposes a constraint which tightens the bound and thereby improves generalization. The experimental results demonstrate superior performance compared to the existing baselines in handling both time and regional domain graph shifts.

Related papers

• Graph Attention for Heterogeneous Graphs with Positional Encoding [0.0]
  Graph Neural Networks (GNNs) have emerged as the de facto standard for modeling graph data.<n>This work benchmarks various GNN architectures to identify the most effective methods for heterogeneous graphs.<n>Our findings reveal that graph attention networks excel in these tasks.
  arXiv  Detail & Related papers  (2025-04-03T18:00:02Z)
• Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation [23.79865440689265]
  Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data.<n>Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain under supervision.<n>We propose the Label-Propagation Graph Neural Network (LP-TGNN) framework to bridge the gap between graph data and traditional domain adaptation methods.
  arXiv  Detail & Related papers  (2025-02-12T15:36:38Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees [50.78679002846741]
  We propose a novel approach to cross-task generalization in graphs via task-trees.<n>We show that pretraining a graph neural network (GNN) on diverse task-trees with a reconstruction objective induces transferable knowledge.<n>This enables efficient adaptation to downstream tasks with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-12-21T02:07:43Z)
• Rethinking Propagation for Unsupervised Graph Domain Adaptation [17.443218657417454]
  Unlabelled Graph Domain Adaptation (UGDA) aims to transfer knowledge from a labelled source graph to an unsupervised target graph. We propose a simple yet effective approach called A2GNN for graph domain adaptation.
  arXiv  Detail & Related papers  (2024-02-08T13:24:57Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Learning to Drop: Robust Graph Neural Network via Topological Denoising [50.81722989898142]
  We propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of Graph Neural Networks (GNNs) PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. We show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
  arXiv  Detail & Related papers  (2020-11-13T18:53:21Z)
• Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs [54.13919050090926]
  We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
  arXiv  Detail & Related papers  (2020-05-15T09:17:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.