The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
DGCNN: A convolutional neural network over large-scale labeled graphs
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.
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206757 · Cited by 2,828
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628
gSpan: graph-based substructure pattern mining
Yan, X., & Han, J. (2003) · 2002 IEEE International Conference on Data Mining, 2002. Proceedings. · doi:10.1109/icdm.2002.1184038 · Cited by 2,013
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 1,673
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Socher, R., Pennington, J., Huang, E., Ng, A. Y., & Manning, C. D. (2011) · Cited by 1,179
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On Graph Kernels: Hardness Results and Efficient Alternatives
Gärtner, T., Flach, P., & Wrobel, S. (2003) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-45167-9_11 · Cited by 879
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Metadata
- Authors
- Phan, A. V., Nguyen, M. L., Nguyen, Y. L. H., & Bui, L. T.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 108
- Pages
- 533-543
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2889895098
- Cited by
- 355
- References
- 34
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
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