Graph Generation

100 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

Greatest papers with code

Scalable Deep Generative Modeling for Sparse Graphs

google-research/google-research ICML 2020

Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$.

Graph Generation

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

deepmind/deepmind-research NAACL (TextGraphs) 2021

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.

Conditional Text Generation Graph Generation +2

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

benedekrozemberczki/pytorch_geometric_temporal NeurIPS 2020

We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.

Graph Generation Multivariate Time Series Forecasting +4

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

divelab/DIG 23 Mar 2021

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.

Graph Generation Self-Supervised Learning

GraphEBM: Molecular Graph Generation with Energy-Based Models

divelab/DIG ICLR Workshop EBM 2021

We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.

Graph Generation Molecular Graph Generation

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.

Graph Generation Scene Graph Generation

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".

Causal Inference Graph Generation +1

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.

Graph Generation Scene Graph Generation +2

Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations

benedekrozemberczki/littleballoffur ‎‎‏‏‎ ‎ 2020

We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.

Graph Generation

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.

Graph Generation Molecular Graph Generation