Mixed Graph Contrastive Network for Semi-Supervised Node Classification

• URL: http://arxiv.org/abs/2206.02796v3
• Date: Thu, 06 Mar 2025 09:10:18 GMT
• Title: Mixed Graph Contrastive Network for Semi-Supervised Node Classification
• Authors: Xihong Yang, Yiqi Wang, Yue Liu, Yi Wen, Lingyuan Meng, Sihang Zhou, Xinwang Liu, En Zhu,
• Abstract summary: We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
• Score: 63.924129159538076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.

Related papers

• Self-Supervised Conditional Distribution Learning on Graphs [15.730933577970687]
  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. This alignment effectively reduces the risk of disrupting intrinsic semantic information through graph-structured data augmentation.
  arXiv  Detail & Related papers  (2024-11-20T07:26:36Z)
• GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning [0.0]
  Graph representation learning has emerged as a powerful tool for preserving graph topology when mapping nodes to vector representations. Current graph neural network models face the challenge of requiring extensive labeled data. We propose Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning.
  arXiv  Detail & Related papers  (2024-09-12T03:09:05Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• A Simplified Framework for Contrastive Learning for Node Representations [2.277447144331876]
  We investigate the potential of deploying contrastive learning in combination with Graph Neural Networks for embedding nodes in a graph. We show that the quality of the resulting embeddings and training time can be significantly improved by a simple column-wise postprocessing of the embedding matrix. This modification yields improvements in downstream classification tasks of up to 1.5% and even beats existing state-of-the-art approaches on 6 out of 8 different benchmarks.
  arXiv  Detail & Related papers  (2023-05-01T02:04:36Z)
• Data-heterogeneity-aware Mixing for Decentralized Learning [63.83913592085953]
  We characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. Motivated by our analysis, we propose an approach that periodically and efficiently optimize the metric.
  arXiv  Detail & Related papers  (2022-04-13T15:54:35Z)
• Improved Dual Correlation Reduction Network [40.792587861237166]
  We propose a novel deep graph clustering algorithm termed Improved Dual Correlation Reduction Network (IDCRN) By approximating the cross-view feature correlation matrix to an identity matrix, we reduce the redundancy between different dimensions of features. We also avoid the collapsed representation caused by the over-smoothing issue in Graph Convolutional Networks (GCNs) through an introduced propagation regularization term.
  arXiv  Detail & Related papers  (2022-02-25T07:48:32Z)
• Geometric Graph Representation Learning via Maximizing Rate Reduction [73.6044873825311]
  Learning node representations benefits various downstream tasks in graph analysis such as community detection and node classification. We propose Geometric Graph Representation Learning (G2R) to learn node representations in an unsupervised manner. G2R maps nodes in distinct groups into different subspaces, while each subspace is compact and different subspaces are dispersed.
  arXiv  Detail & Related papers  (2022-02-13T07:46:24Z)
• Graph Neural Network with Curriculum Learning for Imbalanced Node Classification [21.085314408929058]
  Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. We propose a novel graph neural network framework with curriculum learning (GNN-CL) consisting of two modules.
  arXiv  Detail & Related papers  (2022-02-05T10:46:11Z)
• MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs [55.66953093401889]
  Masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mask a large proportion of edges and try to reconstruct these missing edges during training.
  arXiv  Detail & Related papers  (2022-01-07T16:48:07Z)
• Deep Graph Clustering via Dual Correlation Reduction [37.973072977988494]
  We propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) In our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level. In order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information.
  arXiv  Detail & Related papers  (2021-12-29T04:05:38Z)
• Improving the Training of Graph Neural Networks with Consistency Regularization [9.239633445211574]
  We investigate how consistency regularization can help improve the performance of graph neural networks. We combine the consistency regularization methods with two state-of-the-art GNNs and conduct experiments on the ogbn-products dataset. With the consistency regularization, the performance of state-of-the-art GNNs can be improved by 0.3% on the ogbn-products dataset.
  arXiv  Detail & Related papers  (2021-12-08T14:51:30Z)
• SLGCN: Structure Learning Graph Convolutional Networks for Graphs under Heterophily [5.619890178124606]
  We propose a structure learning graph convolutional networks (SLGCNs) to alleviate the issue from two aspects. Specifically, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representations from all similar nodes. Experimental results on a wide range of benchmark datasets illustrate that the proposed SLGCNs outperform the stat-of-the-art GNN counterparts.
  arXiv  Detail & Related papers  (2021-05-28T13:00:38Z)
• 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)
• Towards Deeper Graph Neural Networks with Differentiable Group Normalization [61.20639338417576]
  Graph neural networks (GNNs) learn the representation of a node by aggregating its neighbors. Over-smoothing is one of the key issues which limit the performance of GNNs as the number of layers increases. We introduce two over-smoothing metrics and a novel technique, i.e., differentiable group normalization (DGN)
  arXiv  Detail & Related papers  (2020-06-12T07:18:02Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.