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DGCNN: A convolutional neural network over large-scale labeled graphs

Phan, A.V. and Nguyen, M.L. and Nguyen, Y.L.H. and Bui, L.T. (2018) DGCNN: A convolutional neural network over large-scale labeled graphs. Neural Networks, 108. pp. 533-543. ISSN 8936080

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Abstract

Exploiting graph-structured data has many real applications in domains including natural language semantics, programming language processing, and malware analysis. A variety of methods has been developed to deal with such data. However, learning graphs of large-scale, varying shapes and sizes is a big challenge for any method. In this paper, we propose a multi-view multi-layer convolutional neural network on labeled directed graphs (DGCNN), in which convolutional filters are designed flexibly to adapt to dynamic structures of local regions inside graphs. The advantages of DGCNN are that we do not need to align vertices between graphs, and that DGCNN can process large-scale dynamic graphs with hundred thousands of nodes. To verify the effectiveness of DGCNN, we conducted experiments on two tasks: malware analysis and software defect prediction. The results show that DGCNN outperforms the baselines, including several deep neural networks. © 2018 Elsevier Ltd

Item Type: Article
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1016/j.neunet.2018.09.001
Uncontrolled Keywords: Computer crime; Convolution; Deep neural networks; Flow graphs; Graphic methods; Malware; Network layers; Neural networks; Semantics; Abstract Syntax Trees; Control flow graphs; Convolutional neural network; Graph structured data; Large-scale dynamics; Natural language semantics; Real applications; Software defect prediction; Trees (mathematics); algorithm; analytic method; Article; artificial neural network; convolutional neural network; large scale production; machine learning; mathematical model; priority journal; semantics; training; computer graphics; natural language processing; trends; Algorithms; Computer Graphics; Natural Language Processing; Neural Networks (Computer); Semantics
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9493

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