Generative and Contrastive Graph Representation Learning

• URL: http://arxiv.org/abs/2505.11776v1
• Date: Sat, 17 May 2025 01:02:22 GMT
• Title: Generative and Contrastive Graph Representation Learning
• Authors: Jiali Chen, Avijit Mukherjee,
• Abstract summary: Self-supervised learning (SSL) on graphs generates node and graph representations that can be used for downstream tasks such as node classification, node clustering, and link prediction.<n>We present a novel architecture for graph SSL that integrates the strengths of both approaches.
• Score: 1.4443417199517135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) on graphs generates node and graph representations (i.e., embeddings) that can be used for downstream tasks such as node classification, node clustering, and link prediction. Graph SSL is particularly useful in scenarios with limited or no labeled data. Existing SSL methods predominantly follow contrastive or generative paradigms, each excelling in different tasks: contrastive methods typically perform well on classification tasks, while generative methods often excel in link prediction. In this paper, we present a novel architecture for graph SSL that integrates the strengths of both approaches. Our framework introduces community-aware node-level contrastive learning, providing more robust and effective positive and negative node pairs generation, alongside graph-level contrastive learning to capture global semantic information. Additionally, we employ a comprehensive augmentation strategy that combines feature masking, node perturbation, and edge perturbation, enabling robust and diverse representation learning. By incorporating these enhancements, our model achieves superior performance across multiple tasks, including node classification, clustering, and link prediction. Evaluations on open benchmark datasets demonstrate that our model outperforms state-of-the-art methods, achieving a performance lift of 0.23%-2.01% depending on the task and dataset.

Related papers

• Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning [0.0]
  Graph representation learning has emerged as a cornerstone for tasks like node classification and link prediction.<n>Current self-supervised learning (SSL) methods face challenges such as computational inefficiency, reliance on contrastive objectives, and representation collapse.<n>We propose a novel joint embedding predictive framework for graph SSL that eliminates contrastive objectives and negative sampling while preserving semantic and structural information.
  arXiv  Detail & Related papers  (2025-02-02T07:42:45Z)
• Improving Node Representation by Boosting Target-Aware Contrastive Loss [10.73390567832967]
  We introduce Target-Aware Contrastive Learning (Target-aware CL) to enhance target task performance. By minimizing XTCL, Target-aware CL increases the mutual information between the target task and node representations. We show experimentally that XTCL significantly improves the performance on two target tasks.
  arXiv  Detail & Related papers  (2024-10-04T20:08:24Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs)<n>We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Cluster-based Graph Collaborative Filtering [55.929052969825825]
  Graph Convolution Networks (GCNs) have succeeded in learning user and item representations for recommendation systems. Most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. We propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF)
  arXiv  Detail & Related papers  (2024-04-16T07:05:16Z)
• Generative-Contrastive Heterogeneous Graph Neural Network [17.889906784627904]
  Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges.<n>Contrastive learning (CL)-based Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential in utilizing data augmentation and contrastive discriminators for downstream tasks.<n>We propose a novel Generative-Contrastive Heterogeneous Graph Neural Network (GC-HGNN)<n> Specifically, we propose a heterogeneous graph generative learning method that enhances CL-based paradigm.
  arXiv  Detail & Related papers  (2024-04-03T15:31:18Z)
• 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)
• Dual Contrastive Attributed Graph Clustering Network [6.796682703663566]
  We propose a generic framework called Dual Contrastive Attributed Graph Clustering Network (DCAGC) In DCAGC, by leveraging Neighborhood Contrast Module, the similarity of the neighbor nodes will be maximized and the quality of the node representation will be improved. All the modules of DCAGC are trained and optimized in a unified framework, so the learned node representation contains clustering-oriented messages.
  arXiv  Detail & Related papers  (2022-06-16T03:17:01Z)
• 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)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Graph Contrastive Learning with Adaptive Augmentation [23.37786673825192]
  We propose a novel graph contrastive representation learning method with adaptive augmentation. Specifically, we design augmentation schemes based on node centrality measures to highlight important connective structures. Our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts.
  arXiv  Detail & Related papers  (2020-10-27T15:12:21Z)
• 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.