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Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
On random graphs I
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Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
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Hardness of approximating graph transformation problem
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Statistical mechanics of complex networks
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Distinguishing enzyme structures from non-enzymes without alignments
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Recursively constructible families of graphs
Noy, M. and Ribó, A · 2004
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Graph evolution: Densification and shrinking diameters
Leskovec, J., Kleinberg, J., and Faloutsos, C · 2007
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An introduction to exponential random graph (p*) models for social networks
Robins, G., Pattison, P., Kalish, Y., and Lusher, D · 2007
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Mixed membership stochastic blockmodels
Airoldi, E., Blei, D., Fienberg, S., and Xing, E · 2008
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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Generative adversarial nets
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Graphite: Iterative generative modeling of graphs
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Learning graphical state transitions
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Molecular de novo design through deep reinforcement learning
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Generating focussed molecule libraries for drug discovery with recurrent neural networks
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