Graph Generation
242 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
Latest papers
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.
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
NVIDIA/DeepLearningExamples
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While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation.
Efficient and Scalable Graph Generation through Iterative Local Expansion
andreasbergmeister/graph-generation
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However, most existing methods struggle with large graphs due to the complexity of representing the entire joint distribution across all node pairs and capturing both global and local graph structures simultaneously.
A Simple and Scalable Representation for Graph Generation
yunhuijang/geel
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Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis.
Panoptic Video Scene Graph Generation
lilydaytoy/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.
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) aims at achieving a comprehensive image understanding by simultaneously segmenting objects and predicting relations among objects.
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models
Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics.
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph Enrichment
jaleedkhan/neusire
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Semantic Web 2023
We present a loosely-coupled neuro-symbolic visual understanding and reasoning framework that employs a DNN-based pipeline for object detection and multi-modal pairwise relationship prediction for scene graph generation and leverages common sense knowledge in heterogenous knowledge graphs to enrich scene graphs for improved downstream reasoning.
Sparse Training of Discrete Diffusion Models for Graph Generation
qym7/sparsediff
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In this work, we introduce SparseDiff, a denoising diffusion model for graph generation that is able to exploit sparsity during its training phase.
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
However, some of its key hurdles include domain generalisation, which is the ability to adapt to previously unseen databases, and alignment of natural language questions with the corresponding SQL queries.
