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 with no code
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semantic overlap.
Graphs Unveiled: Graph Neural Networks and Graph Generation
One of the hot topics in machine learning is the field of GNN.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments.
Towards Scene Graph Anticipation
In SceneSayer, we leverage object-centric representations of relationships to reason about the observed video frames and model the evolution of relationships between objects.
GraphRCG: Self-conditioned Graph Generation via Bootstrapped Representations
In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process.
Graph Generation via Spectral Diffusion
In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks
Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT).
S^2Former-OR: Single-Stage Bimodal Transformer for Scene Graph Generation in OR
In this study, we introduce a novel single-stage bimodal transformer framework for SGG in the OR, termed S^2Former-OR, aimed to complementally leverage multi-view 2D scenes and 3D point clouds for SGG in an end-to-end manner.
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering
In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.
