GraphRNN: A Deep Generative Model for Graphs

By Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec. 2018

108 citations recorded by the source corpus.

Subfields: Generative Models, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

In Graph Learning

Citations in the corpus

Neural Architecture Optimization (2018, background): “It is worthy to mention that for the purpose of generating architecture from a continuous space, one can leverage variational autoencoder [6], or even directly generate the computational graph of an architecture using deep generative models for graph [45, 26].”
Relational inductive biases, deep learning, and graph networks (2018, background): “Recent work has also focused on building generative models of graphs (Li et al., 2018; De Cao and Kipf, 2018; You et al., 2018).”
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, methodology): “You et al. [43] used an auto-regressive model to maximize the likelihood of the graph generation process, but it cannot be used to generate attributed graphs.”
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “On the QED and DRD2 datasets, our models discover new compounds much more frequently than VSeq2Seq, but less often than MMPA and GCPN. Nonetheless, these methods have much lower translation success rate.”
Graphite: Iterative Generative Modeling of Graphs (2018, methodology): “[25] and [55] model graphs as sequences and generate graphs via autoregressive procedures.”
MolGAN: An implicit generative model for small molecular graphs (2018, methodology): “…to explore alternative generative architectures within the Mol-GAN framework, such as recurrent graph-based generative models (Johnson, 2017; Li et al., 2018b; You et al., 2018), as our current one-shot prediction of the adjacency tensor is most likely feasible only for graphs of small size.”
Hierarchical Generation of Molecular Graphs using Structural Motifs (2020, methodology): “1 Our baselines include state-of-the-art graph generation methods (You et al., 2018a; Liu et al., 2018; Jin et al., 2019).”
Graph convolutional networks: a comprehensive review (2019, background): “Note that in the past few years, many other types of graph neural networks have been proposed, including (but are not limited to): (1) graph auto-encoder [21], (2) graph generative model [22, 23], (3) graph attention model [24, 25], and (4) graph recurrent neural networks [26, 27].”
Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design (2018, background): “In this context, there have been a surge of interest in developing deep generative models for graphs [5, 10, 16, 22, 26, 33, 36], partly fueled by the success of generative adversarial networks (GANs) [3, 9] and variational autoencoders (VAEs) [20, 31] in image, audio and text generation [6, 14, 35,”
Neural Execution of Graph Algorithms (2019, methodology): “Graph generation To provide our learner with a wide variety of input graph structure types, we follow prior work [30, 31] and generate graphs from seven categories: Erdős-Rényi [6], BarabásiAlbert [1], grid, 4-community, 4-caveman [26], trees and ladder graphs.”
Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, unknown): “generating molecules with some desired properties [30, 72, 74, 104, 121].”
Learning Discrete Structures for Graph Neural Networks (2019, background): “Recently, deep learning based approaches have been proposed for graph generation (You et al., 2018; Li et al., 2018; Grover et al., 2018; De Cao & Kipf, 2018).”
Constrained Graph Variational Autoencoders for Molecule Design (2018, background): “An alternative approach is to factor the distribution into a sequence of discrete decisions in a graph construction trace [22, 35].”
Mean-field theory of graph neural networks in graph partitioning (2018, methodology): “On the other hand, in GNNs the learning is conducted numerically in a data-driven manner [15], for example by backpropagation.”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, methodology): “, 2021) and a state-of-the-art graph generation method (You et al., 2018) tailored to antibodies.”
Can graph neural networks count substructures? (2020, background): “In recent years, graph neural networks (GNNs) have achieved empirical success on processing data from various fields such as social networks, quantum chemistry, particle physics, knowledge graphs and combinatorial optimization [4, 5, 9, 10, 11, 13, 14, 31, 46, 54, 57, 65, 67, 68, 69, 70, 72, 73].”