Label Contrastive Coding Based Graph Neural Network for Graph Classification

Ren, Yuxiang; Bai, Jiyang; Zhang, Jiawei

doi:10.1007/978-3-030-73194-6_10

Cited by 20 publications

(14 citation statements)

References 9 publications

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“…In addition, the current log data is cumbersome and difficult to label, and how to subtract the reliance on label features in anomaly detection is one of the current difficulties. Therefore, label contrastive coding [38] is introduced to improve the intra-class separability of instance-level by using label contrastive loss.…”

Section: Figure 4: the Framework Of The Hp-sage Modelmentioning

confidence: 99%

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

Fang¹,

Zhao²,

Xu³

et al. 2023

Computers, Materials &Amp; Continua

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System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model named LCC-HGLog, the core of which is the conversion of log anomaly detection into a graph classification problem. Semantic temporal graphs (STG) are constructed by extracting the raw logs' execution sequences and template semantics. Then a unique graph classifier is used to better comprehend each STG's semantic, sequential, and structural features. The classification model is trained jointly by graph classification loss and label contrastive loss. While achieving discriminability at the class-level, it increases the fine-grained identification at the instance-level, thus achieving detection performance even with a small amount of labeled data. We have conducted numerous experiments on real log datasets, showing that the proposed model outperforms the baseline methods and obtains the best all-around performance. Moreover, the detection performance degrades to less than 1% when only 10% of the labeled data is used. With 200 labeled samples, we can achieve the same or better detection results than the baseline methods.

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Section: Figure 4: the Framework Of The Hp-sage Modelmentioning

confidence: 99%

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

Fang¹,

Zhao²,

Xu³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Existing works generally treat node representations or the graph summary as anchors (Velickovic et al 2019;Zeng and Xie 2021;Ren, Bai, and Zhang 2021;Cao et al 2021;Sun et al 2019). For instance, DGI and MVGRL treat the graph summary as anchors, which is first convolved by GCN and then summarized by a readout function.…”

Section: Anchor and Negative Embedding Generationmentioning

confidence: 99%

Simple Unsupervised Graph Representation Learning

Liang

et al. 2022

AAAI

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In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https://github.com/YujieMo/SUGRL.

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“…SUGAR [184] performed subgraph-to-graph contrast, which is to explore the interpretability and semantic connections between substructures and molecular graphs. In addition, graph-to-graph contrast methods [185] , [186] , [187] tried to learn semantics between the augmented graphs in the given dataset. On the other hand, MolCLR [98] , MoCL [97] , KCL [100] , and MICRO-GRAPH [96] leveraged multi-level chemical knowledge where atoms, bond, subgraphs, or graphs can pose in developing chemical properties.…”

Section: Data Augmentation: How To Extend Our Knowledge On Chemical S...mentioning

confidence: 99%

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

Lim¹,

Lee²,

Piao³

et al. 2022

Computational and Structural Biotechnology Journal

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Label Contrastive Coding Based Graph Neural Network for Graph Classification

Cited by 20 publications

References 9 publications

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

Simple Unsupervised Graph Representation Learning

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

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