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Graph Generation
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Graph Generation is an important research area with significant applications in drug and material designs.
Source: Graph Deconvolutional Generation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/A4Bio/GraphsGPT
공식 코드A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
https://github.com/AICPS/roadscene2vec
공식 코드roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs
https://github.com/AdrienC21/CCSD
공식 코드Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential Equations
https://github.com/AndreasOpedal/ba-graph-disentanglement
공식 코드Recovering Barabási-Albert Parameters of Graphs through Disentanglement
https://github.com/BangLiu/StoryForest
공식 코드https://github.com/BlueGhostYi/BIGCF
공식 코드https://github.com/CYVincent/Scene-Graph-Transformer-CogTree
공식 코드CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
https://github.com/DeepGraphLearning/GraphAF
공식 코드GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
https://github.com/Depth2World/VLADBench
공식 코드Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
https://github.com/Dong-JinKim/DenseRelationalCaptioning
공식 코드Dense Relational Image Captioning via Multi-task Triple-Stream Networks
https://github.com/DorianGailhard/HYGENE
공식 코드https://github.com/G-AILab/IGRM
공식 코드https://github.com/IBM/regen
공식 코드https://github.com/IIGROUP/PUM
공식 코드Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation
https://github.com/JanineCHEN/SE-VGAE
공식 코드https://github.com/Jingkang50/OpenPSG
공식 코드https://github.com/JinheonBaek/GMT
공식 코드Accurate Learning of Graph Representations with Graph Multiset Pooling
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch
공식 코드https://github.com/KanghoonYoon/torch-rasgg
공식 코드RA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype Learning
https://github.com/Karim-53/SGG
공식 코드https://github.com/Kenneth-Wong/MMSceneGraph
공식 코드Topic Scene Graph Generation by Attention Distillation from Caption
https://github.com/Kenneth-Wong/het-eccv20
공식 코드Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation
https://github.com/LeiBAI/AGCRN
공식 코드Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
https://github.com/LiuChuang0059/graph-pooling-papers
공식 코드Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
https://github.com/MECLabTUDA/VoxelSceneGraph
공식 코드https://github.com/Mobzhang/PT-DGNN
공식 코드https://github.com/NVIDIA/DeepLearningExamples
공식 코드A Framework for Large Scale Synthetic Graph Dataset Generation
https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform
공식 코드TSPP: A Unified Benchmarking Tool for Time-series Forecasting
https://github.com/PKU-DAIR/FreeHGC
공식 코드Training-free Heterogeneous Graph Condensation via Data Selection
https://github.com/QipengGuo/CycleGT
공식 코드CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
https://github.com/RingBDStack/ELCO
공식 코드Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
https://github.com/Scarecrow0/BGNN-SGG
공식 코드Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
https://github.com/Wayfear/Coacher
공식 코드Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
https://github.com/Wq23333/DLL
공식 코드https://github.com/Yuqifan1117/CaCao
공식 코드Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.