Comparison of Program Representations on Vulnerability Detection with Graph Neural Networks

Yoola Choi; Young-Woo Kwon

doi:10.5573/ieiespc.2021.10.6.477

Comparison of Program Representations on Vulnerability Detection with Graph Neural Networks

IEIE Transactions on Smart Processing and Computing ◽

10.5573/ieiespc.2021.10.6.477 ◽

2021 ◽

Vol 10 (6) ◽

pp. 477-482

Author(s):

Yoola Choi ◽

Young-Woo Kwon

Keyword(s):

Neural Networks ◽

Vulnerability Detection ◽

Graph Neural Networks

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Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2021.3095196 ◽

2021 ◽

pp. 1-1

Author(s):

Zhenguang Liu ◽

Peng Qian ◽

Xiaoyang Wang ◽

Yuan Zhuang ◽

Lin Qiu ◽

...

Keyword(s):

Neural Networks ◽

Expert Knowledge ◽

Vulnerability Detection ◽

Smart Contract ◽

Graph Neural Networks

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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