Negative samples selecting strategy for graph contrastive learning

Miao, Rui; Yang, Yintao; Ma, Yanwei; Juan, Xin; Xue, Haotian; Tang, Jiliang; Wang, Ying; Wang, Xin

doi:10.1016/j.ins.2022.09.024

Cited by 16 publications

(2 citation statements)

References 22 publications

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“…GraphCL [ 37 ], during the data augmentation process, incorporates a comprehensive set of random augmentation strategies, considering both topological structure and node features. On the other hand, the contrastive learning model with a negative sample sampling strategy [ 38 ] effectively converted all nodes except the positive sample into negative samples by selecting nodes with labels different from the center node. In addition, it utilized GCN [ 39 ], SGC [ 40 ], and APPNP [ 41 ] as shared graph neural network models.…”

Section: Related Workmentioning

confidence: 99%

MCGCL:Adversarial attack on graph contrastive learning based on momentum gradient candidates

Zhang,

Qin,

Zhang

2024

PLoS ONE

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In the context of existing adversarial attack schemes based on unsupervised graph contrastive learning, a common issue arises due to the discreteness of graph structures, leading to reduced reliability of structural gradients and consequently resulting in the problem of attacks getting trapped in local optima. An adversarial attack method based on momentum gradient candidates is proposed in this research. Firstly, the gradients obtained by back-propagation are transformed into momentum gradients, and the gradient update is guided by overlaying the previous gradient information in a certain proportion to accelerate convergence speed and improve the accuracy of gradient update. Secondly, the exploratory process of candidate and evaluation is carried out by summing the momentum gradients of the two views and ranking them in descending order of saliency. In this process, selecting adversarial samples with stronger perturbation effects effectively improves the success rate of adversarial attacks. Finally, extensive experiments were conducted on three different datasets, and our generated adversarial samples were evaluated against contrastive learning models across two downstream tasks. The results demonstrate that the attack strategy proposed outperforms existing methods, significantly improving convergence speed. In the link prediction task, targeting the Cora dataset with perturbation rates of 0.05 and 0.1, the attack performance outperforms all baseline tasks, including the supervised baseline methods. The attack method is also transferred to other graph representation models, validating the method’s strong transferability.

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Section: Related Workmentioning

confidence: 99%

MCGCL:Adversarial attack on graph contrastive learning based on momentum gradient candidates

Zhang,

Qin,

Zhang

2024

PLoS ONE

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“…For rolling bearing, obtaining complete labeled data for single and compound faults is a very difficult and costly task, and how to solve the above problem from unlabeled data obtained from experiments or production has become a new topic in bearing-rotor system fault diagnosis [ 19 ]. GCL is a self-supervised learning algorithm for graph data, which aims to train a graph encoder on a given large amount of unlabeled graph data to obtain the feature representation vector of the graph [ 20 ]. The general process is similar to traditional CL, with the advantage of data augmentation of graph signals and contrast hierarchy enhancement [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

et al. 2023

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Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals. In this technique, 10 categories of 1D vibration signals from rolling bearings are sampled using a sliding window approach. The sampled data is then subjected to wavelet packet decomposition (WPD), and the wavelet energy from the final layer of the four-level WPD decomposition in each frequency band is used as the node feature. The weights of edges between nodes are calculated using the Pearson correlation coefficient (PCC) to construct a node graph that describes the feature information of rolling bearings under different health conditions. Data augmentation of the node graph in the dataset is performed by randomly adding nodes and edges. The graph convolutional neural network (GCN) is employed to encode the augmented node graph representation, and deep graph contrastive learning (DGCL) is utilized for the pre-training and classification of the node graph. Experimental results demonstrate that this method outperforms contrastive learning-based fault diagnosis methods for rolling bearings and enables rapid fault diagnosis, thus ensuring the normal operation of mechanical systems. The proposed WPDPCC-DGCL method offers two advantages: (1) the flexibility of wavelet packet decomposition in handling non-smooth vibration signals and combining it with the powerful multi-scale feature encoding capability of GCN for richer characterization of fault information, and (2) the construction of graph node-level fault samples to effectively capture underlying fault information. The experimental results demonstrate the superiority of this method in rolling bearing fault diagnosis over contrastive learning-based approaches, enabling fast and accurate fault diagnoses for rolling bearings and ensuring the normal operation of mechanical systems.

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Contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text classification

Ai,

Li,

Wang

et al. 2025

Expert Systems with Applications

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Negative samples selecting strategy for graph contrastive learning

Cited by 16 publications

References 22 publications

MCGCL:Adversarial attack on graph contrastive learning based on momentum gradient candidates

MCGCL:Adversarial attack on graph contrastive learning based on momentum gradient candidates

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

Contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text classification

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