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We introduce the controllable graph generation problem, formulated as controlling graph attributes during the generative process to produce desired graphs with understandable structures.
On the evolution of random graphs
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Synthesizing test data for fraud detection systems
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Conditional generative adversarial nets
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Semi-synthetic data set generation for security software evaluation
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Interaction networks for learning about objects, relations and physics
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NIPS 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
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Node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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Can GAN learn topological features of a graph?
Liu, W., Chen, P.-Y., Cooper, H., Oh, M. H., Yeung, S., and Suzumura, T · 2017
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Learning social graph topologies using generative adversarial neural networks
Tavakoli, S., Hajibagheri, A., and Sukthankar, G · 2017
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NetGAN: Generating graphs via random walks
Bojchevski, A., Shchur, O., Zügner, D., and Günnemann, S · 2018
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MolGAN: An implicit generative model for small molecular graphs
Cao, N. D. and Kipf, T · 2018
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Local event forecasting and synthesis using unpaired deep graph translations
Gao, Y., Guo, X., and Zhao, L · 2018
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GraphRNN: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Yu, B., Yin, H., and Zhu, Z · 2018
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Graphite: Iterative generative modeling of graphs
Grover, A., Zweig, A., and Ermon, S · 2019
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Deep multi-attributed graph translation with node-edge co-evolution
Guo, X., Zhao, L., Nowzari, C., Rafatirad, S., Homayoun, H., and Pudukotai Dinakarrao, S. M · 2019
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Learning multimodal graph-to-graph translation for molecule optimization
Jin, W., Yang, K., Barzilay, R., and Jaakkola, T · 2019
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Graph to graph: a topology aware approach for graph structures learning and generation
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Guo, X., Wu, L., and Zhao, L · 2018
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Constrained generation of semantically valid graphs via regularizing variational autoencoders
Ma, T., Chen, J., and Xiao, C · 2018
Cited alongside, same era.
GraphVAE: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N · 2018
Cited alongside, same era.
GraphGAN: Graph representation learning with generative adversarial nets
Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X., and Guo, M · 2018
Cited alongside, same era.
Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y
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Sun, M. and Li, P · 2019
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Conditional structure generation through graph variational generative adversarial nets
Yang, C., Zhuang, P., Shi, W., Luu, A., and Li, P · 2019
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Misc-GAN: A multi-scale generative model for graphs
Zhou, D., Zheng, L., Xu, J., and He, J · 2019
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Encoding robust representation for graph generation
Zou, D. and Lerman, G · 2019
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