awesome-graph-generation
https://github.com/yuanqidu/awesome-graph-generation
Last synced: 2 days ago
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Survey
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Graph Generators: State of the art and open challenges
- A Survey on Deep Graph Generation: Methods and Applications
- Deep graph generators: A survey
- A Systematic Survey on Deep Generative Models for Graph Generation
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Generative Diffusion Models on Graphs: Methods and Applications
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Graph Generators: State of the art and open challenges
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
- Machine Learning-Aided Generative Molecular Design
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Algorithm
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- DiGress: Discrete Denoising diffusion for graph generation
- Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
- GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
- Disentangled Spatiotemporal Graph Generative Models
- Deep Generative Model for Periodic Graphs
- AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
- Evaluating Graph Generative Models with Contrastively Learned Features
- Symmetry-induced Disentanglement on Graphs
- An Unpooling Layer for Graph Generation
- Deep graph translation
- SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
- Top-N: Equivariant Set and Graph Generation without Exchangeability
- Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
- TD-GEN: Graph Generation Using Tree Decomposition
- Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
- GraphDF: A discrete flow model for molecular graph generation
- Multi-MotifGAN (MMGAN): Motif-Targeted graph generation and prediction
- Node-edge co-disentangled representation learning for attributed graph generation
- Unsupervised joint k-node graph representations with compositional energy-based models
- Scalable Deep Generative Modeling for Sparse Graphs
- Network principled deep generative models for designing drug combinations as graph sets
- Permutation invariant graph generation via score-Based generative modeling
- Edge-based sequential graph generation with recurrent neural networks
- Graph deconvolutional generation
- Efficient graph generation with graph recurrent attention networks
- Graphite: Iterative Generative Modeling of Graphs
- Deep Q-Learning for directed acyclic graph generation
- Decoding molecular graph embeddings with reinforcement learning
- Can NetGAN be improved on short random walks?
- D-vae: A variational autoencoder for directed acyclic graphs
- Graph normalizing flows
- Graph generation by sequential edge prediction
- Encoding robust representation for graph generation
- Labeled graph generative adversarial networks
- Explore Deep Graph Generation
- Graph generation with variational recurrent neural network
- Learning deep generative models of graphs
- Constrained generation of semantically valid graphs via regularizing variational autoencoders
- Graph convolutional policy network for goal-directed molecular graph generation
- Variational graph auto-encoders
- Defactor: Differentiable edge factorization-based probabilistic graph generation
- GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
- NetGAN: Generating Graphs via Random Walks
- GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
- Syntax-directed variational autoencoder for structured data
- MolGAN: An implicit generative model for small molecular graphs
- DiPol-GAN: Generating Molecular Graphs Adversarially with Relational Differentiable Pooling
- Scene graph generation by iterative message passing
- BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
- gMark: Schema-Driven Generation of Graphs and Queries
- Composing graphical models with neural networks for structured representations and fast inference
- A synthetic data generator for online social network graphs
- A dynamic multiagent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps
- Learning Structured Output Representation using Deep Conditional Generative Models
- Graph-based statistical language model for code
- A modularity-based random SAT instances generator
- Structured generative models of natural source code
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- Fast random walk graph kernel
- An Efficient Generator for Clustered Dynamic Random Networks
- Kronecker graphs: An approach to modeling networks
- RTG: a recursive realistic graph generator using random typing
- Generation and Analysis of Large Synthetic Social Contact Networks
- RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs
- Recent developments in exponential random graph (p*) models for social networks
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- On the evolution of random graphs
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Deep graph translation
- Multi-MotifGAN (MMGAN): Motif-Targeted graph generation and prediction
- Can NetGAN be improved on short random walks?
- Encoding robust representation for graph generation
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- STGGAN: Spatial-temporal graph generation
- Network principled deep generative models for designing drug combinations as graph sets
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Collective dynamics of ‘smallworld’ networks
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Network principled deep generative models for designing drug combinations as graph sets
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Network principled deep generative models for designing drug combinations as graph sets
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Efficient and Scalable Graph Generation through Iterative Local Expansion
- Sparse Training of Discrete Diffusion Models for Graph Generation
- Autoregressive Diffusion Model for Graph Generation
- MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
- Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
- Graph Generative Model for Benchmarking Graph Neural Networks
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Deep generative models for spatial networks
- Interpretable deep graph generation with node-edge co-disentanglement
- S3G2: A Scalable Structure-Correlated Social Graph Generator
- An Efficient Generator for Clustered Dynamic Random Networks
- RTG: a recursive realistic graph generator using random typing
- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication
- Variational Flow Matching for Graph Generation
- Doob’s Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
- Navigating Chemical Space with Latent Flows
- DeFoG: Discrete Flow Matching for Graph Generation
- Fisher Flow Matching for Generative Modeling over Discrete Data
- Equivariant flow matching
- Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation
- Graph Generative Pre-trained Transformer
- Discrete-state Continuous-time Diffusion for Graph Generation
- Cometh: A continuous-time discrete-state graph diffusion model
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Dataset
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Application
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Chemistry
- Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
- MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
- An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
- Molecule Generation by Principal Subgraph Mining and Assembling
- Exploring Chemical Space with Score-based Out-of-distribution Generation
- Interpretable molecular graph generation via monotonic constraints
- Robust Molecular Image Recognition: A Graph Generation Approach
- Small molecule generation via disentangled representation learning
- Deep latent-variable models for controllable molecule generation
- Spanning Tree-based Graph Generation for Molecules
- GraphEBM: Molecular graph generation with energy-based models
- E(n) Equivariant Normalizing Flows
- Nevae: A deep generative model for molecular graphs
- Mol-CycleGAN: a generative model for molecular optimization
- GraphAF: a flow-based autoregressive model for molecular graph generation
- MoFlow: an invertible flow model for generating molecular graphs
- A deep generative model for fragment-based molecule generation
- A two-step graph convolutional decoder for molecule generation
- MolecularRNN: generating realistic molecular graphs with optimized properties
- Graphnvp: An invertible flow model for generating molecular graphs
- Graph residual flow for molecular graph generation
- Likelihood-free inference and generation of molecular graphs
- Scaffold-based molecular design with a graph generative model
- Constrained graph variational autoencoders for molecule design
- Junction tree variational autoencoder for molecular graph generation
- Geometric Latent Diffusion Models for 3D Molecule Generation
- Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
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Biology
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Social Science
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Engineering
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Resource
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Metrics
- MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
- Curvature Filtrations for Graph Generative Model Evaluation
- Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
- On Evaluation Metrics for Graph Generative Models
- Metrics for graph comparison: A practitioner’s guide
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