Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning

• URL: http://arxiv.org/abs/2411.17063v1
• Date: Tue, 26 Nov 2024 03:01:22 GMT
• Title: Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
• Authors: Xinyi Gao, Yayong Li, Tong Chen, Guanhua Ye, Wentao Zhang, Hongzhi Yin,
• Abstract summary: Graph condensation is a promising solution to synthesize a compact, substitute graph of the large-scale original graph. We introduce Contrastive Graph Condensation (CTGC), which adopts a self-supervised surrogate task to extract critical, causal information from the original graph. CTGC excels in handling various downstream tasks with a limited number of labels, consistently outperforming state-of-the-art GC methods.
• Score: 47.74244053386216
• License:
• Abstract: With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact, substitute graph of the large-scale original graph for efficient GNN training. However, existing GC methods predominantly employ classification as the surrogate task for optimization, thus excessively relying on node labels and constraining their utility in label-sparsity scenarios. More critically, this surrogate task tends to overfit class-specific information within the condensed graph, consequently restricting the generalization capabilities of GC for other downstream tasks. To address these challenges, we introduce Contrastive Graph Condensation (CTGC), which adopts a self-supervised surrogate task to extract critical, causal information from the original graph and enhance the cross-task generalizability of the condensed graph. Specifically, CTGC employs a dual-branch framework to disentangle the generation of the node attributes and graph structures, where a dedicated structural branch is designed to explicitly encode geometric information through nodes' positional embeddings. By implementing an alternating optimization scheme with contrastive loss terms, CTGC promotes the mutual enhancement of both branches and facilitates high-quality graph generation through the model inversion technique. Extensive experiments demonstrate that CTGC excels in handling various downstream tasks with a limited number of labels, consistently outperforming state-of-the-art GC methods.

Related papers

• RobGC: Towards Robust Graph Condensation [61.259453496191696]
  Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning. However, the increasing prevalence of large-scale graphs presents a significant challenge for GNN training due to their computational demands. We propose graph condensation (GC) to generate an informative compact graph that enables efficient training of GNNs while retaining performance.
  arXiv  Detail & Related papers  (2024-06-19T04:14:57Z)
• HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning [24.641827220223682]
  We develop a novel Hierarchical Cluster-based GAE (HC-GAE) that can learn effective structural characteristics for graph data analysis. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.
  arXiv  Detail & Related papers  (2024-05-23T16:08:04Z)
• Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition [56.26113670151363]
  Graph condensation is a data-centric solution to replace the large graph with a small yet informative condensed graph. Existing GC methods suffer from intricate optimization processes, necessitating excessive computing resources. We propose a training-free GC framework termed Class-partitioned Graph Condensation (CGC) CGC achieves state-of-the-art performance with a more efficient condensation process.
  arXiv  Detail & Related papers  (2024-05-22T14:57:09Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning [56.45977379288308]
  Masked autoencoder (MAE) learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph. We propose graph contrastive masked autoencoder (GCMAE) framework to unify MAE and CL.
  arXiv  Detail & Related papers  (2023-10-24T05:06:06Z)
• Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method [8.989218350080844]
  This study introduces an innovative method known as the Graph Transformer Auto-Encoder for Graph Clustering (GTAGC) By melding the Graph Auto-Encoder with the Graph Transformer, GTAGC is adept at capturing global dependencies between nodes. The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
  arXiv  Detail & Related papers  (2023-06-20T06:04:03Z)
• Self-supervised Consensus Representation Learning for Attributed Graph [15.729417511103602]
  We introduce self-supervised learning mechanism to graph representation learning. We propose a novel Self-supervised Consensus Representation Learning framework. Our proposed SCRL method treats graph from two perspectives: topology graph and feature graph.
  arXiv  Detail & Related papers  (2021-08-10T07:53:09Z)
• Self-Constructing Graph Convolutional Networks for Semantic Labeling [23.623276007011373]
  We propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset.
  arXiv  Detail & Related papers  (2020-03-15T21:55:24Z)
• Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions [153.6357310444093]
  Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. We argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. We design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size.
  arXiv  Detail & Related papers  (2020-03-05T13:05:27Z)

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.