Dual-Kernel Graph Community Contrastive Learning

• URL: http://arxiv.org/abs/2511.08287v1
• Date: Wed, 12 Nov 2025 01:50:57 GMT
• Title: Dual-Kernel Graph Community Contrastive Learning
• Authors: Xiang Chen, Kun Yue, Wenjie Liu, Zhenyu Zhang, Liang Duan,
• Abstract summary: Graph Contrastive Learning (GCL) has emerged as a powerful paradigm for training Graph Neural Networks (GNNs)<n>We propose an efficient GCL framework that transforms the input graph into a compact network of interconnected node sets.<n>Our method outperforms state-of-the-art GCL baselines in both effectiveness and scalability.
• Score: 14.92920991249099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful paradigm for training Graph Neural Networks (GNNs) in the absence of task-specific labels. However, its scalability on large-scale graphs is hindered by the intensive message passing mechanism of GNN and the quadratic computational complexity of contrastive loss over positive and negative node pairs. To address these issues, we propose an efficient GCL framework that transforms the input graph into a compact network of interconnected node sets while preserving structural information across communities. We firstly introduce a kernelized graph community contrastive loss with linear complexity, enabling effective information transfer among node sets to capture hierarchical structural information of the graph. We then incorporate a knowledge distillation technique into the decoupled GNN architecture to accelerate inference while maintaining strong generalization performance. Extensive experiments on sixteen real-world datasets of varying scales demonstrate that our method outperforms state-of-the-art GCL baselines in both effectiveness and scalability.

Related papers

• GADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition [6.24191713518868]
  GADPN is a graph structure learning framework that adaptively refines graph topology via low-rank denoising and generalized structural perturbation.<n>It achieves state-of-the-art performance while significantly improving efficiency.<n>It shows particularly strong gains on challenging disassortative graphs, validating its ability to robustly learn enhanced graph structures.
  arXiv  Detail & Related papers  (2026-01-13T05:25:32Z)
• Parallelizing Node-Level Explainability in Graph Neural Networks [0.3262230127283452]
  Graph Neural Networks (GNNs) have demonstrated remarkable performance in a wide range of tasks.<n>In node classification, node-level explainability becomes extremely time-consuming as the size of the graph increases.<n>This paper introduces a novel approach to parallelizing node-level explainability in GNNs through graph partitioning.
  arXiv  Detail & Related papers  (2026-01-08T10:39:48Z)
• Attention Beyond Neighborhoods: Reviving Transformer for Graph Clustering [21.941792185132996]
  Attentive Graph Clustering Network (AGCN) is a novel architecture that reinterprets the notion that graph is attention.<n>AGCN embeds the attention mechanism into the graph structure, enabling effective global information extraction.<n>Our framework incorporates theoretical analysis to contrast AGCN behavior with Graph Neural Networks (GNNs) and Transformer.
  arXiv  Detail & Related papers  (2025-09-18T14:51:13Z)
• ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion [73.85920403511706]
  We propose ScaleGNN, a novel framework that adaptively fuses multi-hop node features for scalable and effective graph learning.<n>We show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-22T14:05:11Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)
• Conditional Distribution Learning on Graphs [15.730933577970687]
  We propose a conditional distribution learning (CDL) method that learns graph representations from graph-structured data for semisupervised graph classification.<n>Specifically, we present an end-to-end graph representation learning model to align the conditional distributions of weakly and strongly augmented features over the original features.
  arXiv  Detail & Related papers  (2024-11-20T07:26:36Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• Learning Strong Graph Neural Networks with Weak Information [64.64996100343602]
  We develop a principled approach to the problem of graph learning with weak information (GLWI) We propose D$2$PT, a dual-channel GNN framework that performs long-range information propagation on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
  arXiv  Detail & Related papers  (2023-05-29T04:51:09Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Adversarial Graph Augmentation to Improve Graph Contrastive Learning [21.54343383921459]
  We propose a novel principle, termed adversarial-GCL (AD-GCL), which enables GNNs to avoid capturing redundant information during the training. We experimentally validate AD-GCL by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to $14%$ in unsupervised, $6%$ in transfer, and $3%$ in semi-supervised learning settings.
  arXiv  Detail & Related papers  (2021-06-10T15:34:26Z)

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.