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
Knowledge-Embedded Routing Network for Scene Graph Generation
More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
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.
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.
Molecule Generation by Principal Subgraph Mining and Assembling
thunlp-mt/ps-vae
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Molecule generation is central to a variety of applications.
FastFlows: Flow-Based Models for Molecular Graph Generation
aspuru-guzik-group/selfies
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We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
bioinfomachinelearning/bio-diffusion
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However, such methods are unable to learn important geometric and physical properties of 3D molecules during molecular graph generation, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which negatively impacts their ability to effectively scale to datasets of large 3D molecules.
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
jacobyuan7/rlipv2
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ICCV 2023
In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.
Panoptic Video Scene Graph Generation
jingkang50/openpvsg
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CVPR 2023
PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
NetGAN: Generating Graphs via Random Walks
danielzuegner/netgan
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ICML 2018
NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.
