A Deep Generative Model for Graph Layoutdoi:10.1109/TVCG.2019.2934396Kwan-Liu MaOh-Hyun Kwon
Molecule optimization is a critical step in drug development to improve the desired properties of drug candidates through chemical modification. We have developed a novel deep generative model, Modof, over molecular graphs for molecule optimization. Modo
其中embedding分为如下几步: Atom Embedding over Graphs (GMPN) Modof使用一个one-hot xi 来表示原子 ai 的类型,使用一个热编码 xij 来表示连接 ai 和aj 的键bij 的类型。每个键 bij 与两个信息 mij 和mji 相关联,它们编码从原子 ai 传播到 aj 的信息,反之亦然。 Node Embedding over Junction Trees (...
Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a...
At first, we discuss the following five representative deep generative models, which aims to learn the probability distribution of graphs so that we can sample new graphs from it. 首先,我们讨论了以下五个具有代表性的深度生成模型,旨在学习图的概率分布,以便我们可以从中采样新的图。 Auto-Regressive mo...
深度递归网络的嵌入Deep Recursive Network Embedding (DRNE) 图生成网络 Graph Generative Networks 分子生成对抗网络 Molecular Generative Adversarial Networks (MolGAN) Deep Generative Models of Graphs (DGMG) 图时空网络Graph Spatial-Temporal Networks
Recent studies reveal diffusion model can be used for real image and video editing in a similar way. Please refer to Diffusion Inversion and Diffusion Latent Space Editing for more details. Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model. ...
a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from...
19、Metropolis-hastings data augmentation for graph neural networks. NeurIPS, 2021. 20、Graphrnn: Generating realistic graphs with deep auto-regressive models. In ICML, 2018. 21、Netgan:Generating graphs via random walks. In ICML, 2018. 22、Learning discrete structures for graph neural networks. In...
We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine. Our analysis is based on learning the distribution of ...