A Practical Tutorial on Graph Neural Networks

Ward, Isaac Ronald; Joyner, Jack; Lickfold, Casey; Guo, Yulan; Bennamoun, Mohammed

doi:10.1145/3503043

Cited by 6 publications

(1 citation statement)

References 53 publications

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“…Also, it might be interesting to consider a graphical representation of the landscape components after segmentation, including green objects in a garden, for evaluation in terms of the quality of the green space. Graph neural networks (GNNs) [102] could be properly exploited for the representation and evaluation in terms of the diverse qualitative criteria of landscape analysis.…”

Section: Other Types Of Qualitative Landscape Analysismentioning

confidence: 99%

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

Lee,

Park,

Lee

2024

Sensors

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Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

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Section: Other Types Of Qualitative Landscape Analysismentioning

confidence: 99%

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

Lee,

Park,

Lee

2024

Sensors

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FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection

Bakar,

De Marinis,

Cugini

et al. 2024

Computer Networks

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Graph Neural Network for Source Code Defect Prediction

et al. 2022

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Predicting defective software modules before testing is a useful operation that ensures that the time and cost of software testing can be reduced. In recent years, several models have been proposed for this purpose, most of which are built using deep learning-based methods. However, most of these models do not take full advantage of a source code as they ignore its tree structure or they focus only on a small part of a code. To investigate whether and to what extent information from this structure can be beneficial in predicting defective source code, we developed an end-to-end model based on a convolutional graph neural network (GCNN) for defect prediction, whose architecture can be adapted to the analyzed software, so that projects of different sizes can be processed with the same level of detail. The model processes the information of the nodes and edges from the abstract syntax tree (AST) of the source code of a software module and classifies the module as defective or not defective based on this information. Experiments on open source projects written in Java have shown that the proposed model performs significantly better than traditional defect prediction models in terms of AUC and F-score. Based on the F-scores of the existing state-of-the-art models, the model has shown comparable predictive capabilities for the analyzed projects.INDEX TERMS Software defect prediction, deep learning, graph neural network.

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A Practical Tutorial on Graph Neural Networks

Cited by 6 publications

References 53 publications

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection

Graph Neural Network for Source Code Defect Prediction

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