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

Source: Graph Deconvolutional Generation

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Greatest papers with code

Scalable Deep Generative Modeling for Sparse Graphs

ICML 2020 google-research/google-research

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

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

23 Mar 2021divelab/DIG

To facilitate graph deep learning research, we introduce DIG: Dive into Graphs, a research-oriented library that integrates unified and extensible implementations of common graph deep learning algorithms for several advanced tasks.

GRAPH GENERATION SELF-SUPERVISED LEARNING

GraphEBM: Molecular Graph Generation with Energy-Based Models

31 Jan 2021divelab/DIG

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

ECCV 2018 jwyang/graph-rcnn.pytorch

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

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

‎‎‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

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

Unbiased Scene Graph Generation from Biased Training

CVPR 2020 KaihuaTang/Scene-Graph-Benchmark.pytorch

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 SCENE GRAPH GENERATION

Learning to Compose Dynamic Tree Structures for Visual Contexts

CVPR 2019 KaihuaTang/Scene-Graph-Benchmark.pytorch

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 VISUAL QUESTION ANSWERING VISUAL REASONING

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

28 May 2019pfnet-research/chainer-chemistry

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

GRAPH GENERATION MOLECULAR GRAPH GENERATION

Learning Deep Generative Models of Graphs

ICLR 2018 JiaxuanYou/graph-generation

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.

GRAPH GENERATION KNOWLEDGE GRAPHS

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

ICML 2018 JiaxuanYou/graph-generation

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.

GRAPH GENERATION