Papers with Code 아카이브
Graph Neural Network
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/basiralab/AGSR-Net
공식 코드https://github.com/basiralab/GNNs-in-Network-Neuroscience
공식 코드https://github.com/basiralab/MGN-Net
공식 코드MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
https://github.com/basiralab/RBGM
공식 코드Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory
https://github.com/basiralab/deltagnn
공식 코드DeltaGNN: Graph Neural Network with Information Flow Control
https://github.com/basiralab/duognn
공식 코드DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
https://github.com/basiralab/falcon
공식 코드FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs
https://github.com/basiralab/meta-reggnn
공식 코드https://github.com/basiralab/multigraphgnet
공식 코드https://github.com/basiralab/topoGAN
공식 코드Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network
https://github.com/bayer-science-for-a-better-life/graph-attribution
공식 코드Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
https://github.com/bcv-uniandes/sepal
공식 코드https://github.com/bdi-lab/reed
공식 코드PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
https://github.com/benatorc/OTGNN
공식 코드https://github.com/benbogin/spider-schema-gnn
공식 코드Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
https://github.com/benbogin/spider-schema-gnn-global
공식 코드Global Reasoning over Database Structures for Text-to-SQL Parsing
https://github.com/benedekrozemberczki/pytorch_geometric_temporal
공식 코드https://github.com/benyeoh/grownup
공식 코드GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training
https://github.com/bhanu068/lagnn
공식 코드Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding
https://github.com/bharat-runwal/rwl-gnn
공식 코드https://github.com/bigheiniu/grenade
공식 코드https://github.com/bigvideoresearch/Enigma
공식 코드https://github.com/binghong-ml/retro_star
공식 코드https://github.com/bioinfomachinelearning/gcpnet
공식 코드Geometry-Complete Perceptron Networks for 3D Molecular Graphs
https://github.com/biomed-AI/MolRep
공식 코드Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
https://github.com/biomedicalinformaticsgroup/gnn-suite
공식 코드GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics
https://github.com/bishalth01/BrainRGIN
공식 코드Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
https://github.com/bit-da/lsg
공식 코드https://github.com/bjtu-ccd-lab/corrstn
공식 코드A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
https://github.com/blt2114/protdiff_smcdiff
공식 코드Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
https://github.com/bnn-upc/graphlaxy
공식 코드Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation
https://github.com/bobowner/open-world-lgl
공식 코드https://github.com/bojack-horseman91/GraphAge
공식 코드GraphAge: Unleashing the power of Graph Neural Network to Decode Epigenetic Aging
https://github.com/bokwaiho/itipr
공식 코드https://github.com/bowenwang77/DR-Label
공식 코드DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.