Graph Generation
241 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
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
bowenliu16/rl_graph_generation
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NeurIPS 2018
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning
paulgay/VGfM
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Recent approaches on visual scene understanding attempt to build a scene graph -- a computational representation of objects and their pairwise relationships.
Efficient Graph Generation with Graph Recurrent Attention Networks
lrjconan/GRAN
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NeurIPS 2019
Our model generates graphs one block of nodes and associated edges at a time.
Hierarchical Generation of Molecular Graphs using Structural Motifs
wengong-jin/hgraph2graph
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ICML 2020
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
Relation Transformer Network
In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
QipengGuo/CycleGT
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ACL (WebNLG, INLG) 2020
Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.
Learning Visual Commonsense for Robust Scene Graph Generation
ZhecanJamesWang/GLAT_SGG
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ECCV 2020
Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild.
GPT-GNN: Generative Pre-Training of Graph Neural Networks
acbull/GPT-GNN
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Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
mobarakol/Surgical_SceneGraph_Generation
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Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.
Multiresolution Equivariant Graph Variational Autoencoder
In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner.
