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
Libraries
Use these libraries to find Graph Generation models and implementationsLatest papers
EGTR: Extracting Graph from Transformer for Scene Graph Generation
We propose a lightweight one-stage SGG model that extracts the relation graph from the various relationships learned in the multi-head self-attention layers of the DETR decoder.
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation
Recent studies, which employ pre-trained language models to auto-regressively generate linearised graphs for constructing event temporal graphs, have shown promising results.
SteinGen: Generating Fidelitous and Diverse Graph Samples
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small.
3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of Molecular Graphs
However, practical applications call for methods that generate diverse, and ideally novel, molecules with the desired properties.
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps.
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.
Graph Diffusion Policy Optimization
Recent research has made significant progress in optimizing diffusion models for specific downstream objectives, which is an important pursuit in fields such as graph generation for drug design.
Diffusion-based graph generative methods
Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks.
SGTR+: End-to-end Scene Graph Generation with Transformer
Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner.
Adaptive Self-training Framework for Fine-grained Scene Graph Generation
To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained.