Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation

• URL: http://arxiv.org/abs/2402.10468v1
• Date: Fri, 16 Feb 2024 06:17:50 GMT
• Title: Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
• Authors: Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo, Xiangyu Zhao
• Abstract summary: ACGCL capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity. Within the ACGCL framework, we have devised a novel adversarial curriculum training methodology. A comprehensive assessment of ACGCL is conducted through extensive experiments on six well-known benchmark datasets.
• Score: 35.875976206333185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their resemblance to the original data. Nevertheless, precise control over similarity during sample generation presents a formidable challenge, often impeding the effective discovery of representative graph patterns. To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein. Within the ACGCL framework, we have devised a novel adversarial curriculum training methodology that facilitates progressive learning by sequentially increasing the difficulty of distinguishing the generated samples. Notably, this approach transcends the prevalent sparsity issue inherent in conventional curriculum learning strategies by adaptively concentrating on more challenging training data. Finally, a comprehensive assessment of ACGCL is conducted through extensive experiments on six well-known benchmark datasets, wherein ACGCL conspicuously surpasses a set of state-of-the-art baselines.

Related papers

• Dual Adversarial Perturbators Generate rich Views for Recommendation [16.284670207195056]
  AvoGCL emulates curriculum learning by applying adversarial training to graph structures and embedding perturbations. Experiments on three real-world datasets demonstrate that AvoGCL significantly outperforms the state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-26T15:19:35Z)
• Disentangled Generative Graph Representation Learning [51.59824683232925]
  This paper introduces DiGGR (Disentangled Generative Graph Representation Learning), a self-supervised learning framework. It aims to learn latent disentangled factors and utilize them to guide graph mask modeling. Experiments on 11 public datasets for two different graph learning tasks demonstrate that DiGGR consistently outperforms many previous self-supervised methods.
  arXiv  Detail & Related papers  (2024-08-24T05:13:02Z)
• Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance [25.5510013711661]
  We propose the Clustering-guided Curriculum Graph contrastive Learning (CCGL) framework. CCGL uses clustering entropy as the guidance of the following graph augmentation and contrastive learning. Experimental results demonstrate that CCGL has achieved excellent performance compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-22T02:18:47Z)
• Graph-level Protein Representation Learning by Structure Knowledge Refinement [50.775264276189695]
  This paper focuses on learning representation on the whole graph level in an unsupervised manner. We propose a novel framework called Structure Knowledge Refinement (SKR) which uses data structure to determine the probability of whether a pair is positive or negative.
  arXiv  Detail & Related papers  (2024-01-05T09:05:33Z)
• Rethinking and Simplifying Bootstrapped Graph Latents [48.76934123429186]
  Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning. We present SGCL, a simple yet effective GCL framework that utilizes the outputs from two consecutive iterations as positive pairs. We show that SGCL can achieve competitive performance with fewer parameters, lower time and space costs, and significant convergence speedup.
  arXiv  Detail & Related papers  (2023-12-05T09:49:50Z)
• On the Adversarial Robustness of Graph Contrastive Learning Methods [9.675856264585278]
  We introduce a comprehensive evaluation robustness protocol tailored to assess the robustness of graph contrastive learning (GCL) models. We subject these models to adaptive adversarial attacks targeting the graph structure, specifically in the evasion scenario. With our work, we aim to offer insights into the robustness of GCL methods and hope to open avenues for potential future research directions.
  arXiv  Detail & Related papers  (2023-11-29T17:59:18Z)
• 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)
• Uncovering the Structural Fairness in Graph Contrastive Learning [87.65091052291544]
  Graph contrastive learning (GCL) has emerged as a promising self-supervised approach for learning node representations. We show that representations obtained by GCL methods are already fairer to degree bias than those learned by GCN. We devise a novel graph augmentation method, called GRAph contrastive learning for DEgree bias (GRADE), which applies different strategies to low- and high-degree nodes.
  arXiv  Detail & Related papers  (2022-10-06T15:58:25Z)
• Adversarial Cross-View Disentangled Graph Contrastive Learning [30.97720522293301]
  We introduce ACDGCL, which follows the information bottleneck principle to learn minimal yet sufficient representations from graph data. We empirically demonstrate that our proposed model outperforms the state-of-the-arts on graph classification task over multiple benchmark datasets.
  arXiv  Detail & Related papers  (2022-09-16T03:48:39Z)
• Diversified Multiscale Graph Learning with Graph Self-Correction [55.43696999424127]
  We propose a diversified multiscale graph learning model equipped with two core ingredients. A graph self-correction (GSC) mechanism to generate informative embedded graphs, and a diversity boosting regularizer (DBR) to achieve a comprehensive characterization of the input graph. Experiments on popular graph classification benchmarks show that the proposed GSC mechanism leads to significant improvements over state-of-the-art graph pooling methods.
  arXiv  Detail & Related papers  (2021-03-17T16:22:24Z)

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.