Graph Generation
298 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
Benchmarks
These leaderboards are used to track progress in Graph Generation
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Libraries
Use these libraries to find Graph Generation models and implementations
Torque
PISC
WikiGraphs
TRN
Most implemented papers
Junction Tree Variational Autoencoder for Molecular Graph Generation
wengong-jin/icml18-jtnn
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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
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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
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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
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CVPR 2017
In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.
Image-Conditioned Graph Generation for Road Network Extraction
davide-belli/generative-graph-transformer
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For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data.
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Scarecrow0/BGNN-SGG
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CVPR 2021
Scene graph generation is an important visual understanding task with a broad range of vision applications.
Structured Sparse R-CNN for Direct Scene Graph Generation
mcg-nju/structured-sparse-rcnn
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CVPR 2022
The key to our method is a set of learnable triplet queries and a structured triplet detector which could be jointly optimized from the training set in an end-to-end manner.
Pixels to Graphs by Associative Embedding
umich-vl/px2graph
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NeurIPS 2017
Graphs are a useful abstraction of image content.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
snap-stanford/GraphRNN
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ICML 2018
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Graph R-CNN for Scene Graph Generation
jwyang/graph-rcnn.pytorch
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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.
