Graph Generation

151 papers with code • 1 benchmarks • 5 datasets

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation


Use these libraries to find Graph Generation models and implementations

Most implemented papers

Junction Tree Variational Autoencoder for Molecular Graph Generation

wengong-jin/icml18-jtnn ICML 2018

We evaluate our model on multiple tasks ranging from molecular generation to optimization.

Learning to Compose Dynamic Tree Structures for Visual Contexts

KaihuaTang/Scene-Graph-Benchmark.pytorch CVPR 2019

We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A.

Unbiased Scene Graph Generation from Biased Training

KaihuaTang/Scene-Graph-Benchmark.pytorch CVPR 2020

Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".

Scene Graph Generation by Iterative Message Passing

microsoft/scene_graph_benchmark CVPR 2017

In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.

Graph R-CNN for Scene Graph Generation

jwyang/graph-rcnn.pytorch ECCV 2018

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images.

Knowledge-Embedded Routing Network for Scene Graph Generation

yuweihao/KERN CVPR 2019

More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

pfnet-research/chainer-chemistry 28 May 2019

We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.

Image-Conditioned Graph Generation for Road Network Extraction

davide-belli/generative-graph-transformer 31 Oct 2019

For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data.

GPT-GNN: Generative Pre-Training of Graph Neural Networks

acbull/GPT-GNN 27 Jun 2020

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.