Rank and Align: Towards Effective Source-free Graph Domain Adaptation

• URL: http://arxiv.org/abs/2408.12185v1
• Date: Thu, 22 Aug 2024 08:00:50 GMT
• Title: Rank and Align: Towards Effective Source-free Graph Domain Adaptation
• Authors: Junyu Luo, Zhiping Xiao, Yifan Wang, Xiao Luo, Jingyang Yuan, Wei Ju, Langechuan Liu, Ming Zhang,
• Abstract summary: Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. We introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning.
• Score: 16.941755478093153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA.

Related papers

• GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation [13.317620250521124]
  Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world. Recent graph neural network (GNN) approaches can suffer from serious performance decline due to domain shift and label scarcity. We propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA), tailored for source-free graph domain adaptation.
  arXiv  Detail & Related papers  (2024-10-22T01:32:46Z)
• GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning [17.85404473268992]
  Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks. Despite their versatility, GNNs face significant challenges in transferability, limiting their utility in real-world applications. We propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains.
  arXiv  Detail & Related papers  (2024-09-25T06:57:42Z)
• 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)
• Domain Adaptive Graph Classification [0.0]
  We introduce the Dual Adversarial Graph Representation Learning (DAGRL), which explore the graph topology from dual branches and mitigate domain discrepancies via dual adversarial learning. Our approach incorporates adaptive perturbations into the dual branches, which align the source and target distribution to address domain discrepancies.
  arXiv  Detail & Related papers  (2023-12-21T02:37:56Z)
• ALEX: Towards Effective Graph Transfer Learning with Noisy Labels [11.115297917940829]
  We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address the problem of graph transfer learning. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions.
  arXiv  Detail & Related papers  (2023-09-26T04:59:49Z)
• Graph Condensation via Receptive Field Distribution Matching [61.71711656856704]
  This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on the size-reduced graph can make accurate predictions. We view the original graph as a distribution of receptive fields and aim to synthesize a small graph whose receptive fields share a similar distribution.
  arXiv  Detail & Related papers  (2022-06-28T02:10:05Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Source Free Unsupervised Graph Domain Adaptation [60.901775859601685]
  Unsupervised Graph Domain Adaptation (UGDA) shows its practical value of reducing the labeling cost for node classification. Most existing UGDA methods heavily rely on the labeled graph in the source domain. In some real-world scenarios, the source graph is inaccessible because of privacy issues. We propose a novel scenario named Source Free Unsupervised Graph Domain Adaptation (SFUGDA)
  arXiv  Detail & Related papers  (2021-12-02T03:18:18Z)
• Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification [50.899576891296235]
  Convolutional neural networks have been widely applied to hyperspectral image classification. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies.
  arXiv  Detail & Related papers  (2021-06-26T06:24:51Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)

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.