Local Structure-aware Graph Contrastive Representation Learning

• URL: http://arxiv.org/abs/2308.03271v1
• Date: Mon, 7 Aug 2023 03:23:46 GMT
• Title: Local Structure-aware Graph Contrastive Representation Learning
• Authors: Kai Yang, Yuan Liu, Zijuan Zhao, Peijin Ding, Wenqian Zhao
• Abstract summary: We propose a Local Structure-aware Graph Contrastive representation Learning method (LS-GCL) to model the structural information of nodes from multiple views. For the local view, the semantic subgraph of each target node is input into a shared GNN encoder to obtain the target node embeddings at the subgraph-level. For the global view, considering the original graph preserves indispensable semantic information of nodes, we leverage the shared GNN encoder to learn the target node embeddings at the global graph-level.
• Score: 12.554113138406688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainly focus on the feature information of the global graph or small subgraph structure (e.g., the first-order neighborhood). In the paper, we propose a Local Structure-aware Graph Contrastive representation Learning method (LS-GCL) to model the structural information of nodes from multiple views. Specifically, we construct the semantic subgraphs that are not limited to the first-order neighbors. For the local view, the semantic subgraph of each target node is input into a shared GNN encoder to obtain the target node embeddings at the subgraph-level. Then, we use a pooling function to generate the subgraph-level graph embeddings. For the global view, considering the original graph preserves indispensable semantic information of nodes, we leverage the shared GNN encoder to learn the target node embeddings at the global graph-level. The proposed LS-GCL model is optimized to maximize the common information among similar instances at three various perspectives through a multi-level contrastive loss function. Experimental results on five datasets illustrate that our method outperforms state-of-the-art graph representation learning approaches for both node classification and link prediction tasks.

Related papers

• 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)
• Saliency-Aware Regularized Graph Neural Network [39.82009838086267]
  We propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone.
  arXiv  Detail & Related papers  (2024-01-01T13:44:16Z)
• Subgraph Networks Based Contrastive Learning [5.736011243152416]
  Graph contrastive learning (GCL) can solve the problem of annotated data scarcity. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. We propose a novel framework called subgraph network-based contrastive learning (SGNCL)
  arXiv  Detail & Related papers  (2023-06-06T08:52:44Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• 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)
• Distance-wise Graph Contrastive Learning [21.790413668252828]
  Contrastive learning (CL) has proven highly effective in graph-based semi-supervised learning (SSL) We propose our Distance-wise Graph Contrastive Learning (DwGCL) method from two views. Our experiments on five benchmark graph datasets show that DwGCL can bring a clear improvement over previous GCL methods.
  arXiv  Detail & Related papers  (2020-12-14T11:44:45Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• 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)
• Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization [8.14036521415919]
  We present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation (UHGR) Our method focuses on maximizing mutual information between "local" and high-level "global" representations. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks.
  arXiv  Detail & Related papers  (2020-03-18T18:21:48Z)
• Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
  We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification. For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes. Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T02:52:30Z)

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.