Graph neural networks: A review of methods and applications

Zhou, Jie; Cui, Guixiang; Hu, Shengding; Zhang, Zhengyan; Yang, Cheng; Liu, Zhiyuan; Wang, Lifeng; Li, Changcheng; Sun, Maosong

doi:10.1016/j.aiopen.2021.01.001

Cited by 2,720 publications

(1,200 citation statements)

References 117 publications

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“…More details on graph neural networks can be found in the review by Zhou et al [ 108 ]. In the context of molecular prediction, dozens of examples use GNNs, as summarized in the review by Wieder et al [ 109 ].…”

Section: Methods and Datamentioning

confidence: 99%

Deep Learning in Virtual Screening: Recent Applications and Developments

Kimber

Chen

Volkamer

2021

IJMS

107

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Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

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Section: Methods and Datamentioning

confidence: 99%

Deep Learning in Virtual Screening: Recent Applications and Developments

Kimber

Chen

Volkamer

2021

IJMS

107

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“…The random walk-based models for graphs can be divided into many different categories, according to varying perspectives. One possible division for these models includes categorization that is based on their embedding output, for instance, local structure-preserving methods, global structure-preserving methods, and the combination of the two [115].…”

Section: Path and Walk-based Methodsmentioning

confidence: 99%

“…Another extension for graph embedding methods that have become achievable by neural networks, is the embedding of subgraphs (S ⊂ V). The attribute aggregation procedure in different neural network architectures may vary according to their connection types, and the usage of filters or gates in the propagation step of the models [115].…”

Section: Neural Network-based Methodsmentioning

confidence: 99%

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu

Oghaz

Rajabi

et al. 2020

MAKE

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Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

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“…Many graphs and geometric convolution methods have been proposed recently for modeling graph data (Bronstein et al, 2017 ; Hamilton et al, 2017b ; Zhang et al, 2018 ; Zhou et al, 2018 ; Wu et al, 2019 ). The spectral convolution methods (Defferrard et al, 2016 ; Kipf and Welling, 2017 ) are the mainstream algorithms developed as the graph convolution methods.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Graph Learning for Circuit Deobfuscation

et al. 2021

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Circuit obfuscation is a recently proposed defense mechanism to protect the intellectual property (IP) of digital integrated circuits (ICs) from reverse engineering. There have been effective schemes, such as satisfiability (SAT)-checking based attacks that can potentially decrypt obfuscated circuits, which is called deobfuscation. Deobfuscation runtime could be days or years, depending on the layouts of the obfuscated ICs. Hence, accurately pre-estimating the deobfuscation runtime within a reasonable amount of time is crucial for IC designers to optimize their defense. However, it is challenging due to (1) the complexity of graph-structured circuit; (2) the varying-size topology of obfuscated circuits; (3) requirement on efficiency for deobfuscation method. This study proposes a framework that predicts the deobfuscation runtime based on graph deep learning techniques to address the challenges mentioned above. A conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem by analyzing the SAT attack method. Multi-order information of the graph matrix is designed to identify the essential features and reduce the computational cost. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features into an identical vector space. Then, we designed a framework, Deep Survival Analysis with Graph (DSAG), which integrates energy-based layers and predicts runtime inspired by censored regression in survival analysis. Integrating uncensored data with censored data, the proposed model improves the standard regression significantly. DSAG is an end-to-end framework that can automatically extract the determinant features for deobfuscation runtime. Extensive experiments on benchmarks demonstrate its effectiveness and efficiency.

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Graph neural networks: A review of methods and applications

Cited by 2,720 publications

References 117 publications

Deep Learning in Virtual Screening: Recent Applications and Developments

Deep Learning in Virtual Screening: Recent Applications and Developments

Review on Learning and Extracting Graph Features for Link Prediction

Deep Graph Learning for Circuit Deobfuscation

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