Papers with Code 아카이브
Graph Embedding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
<span style="color:grey; opacity: 0.6">( Image credit: GAT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zjukg/cause
공식 코드https://github.com/zjukg/comprehensive-study-over-relational-patterns
공식 코드A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
https://github.com/zjukg/earl
공식 코드Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
https://github.com/zjukg/mans
공식 코드Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
https://github.com/zjukg/morse
공식 코드Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
https://github.com/zjukg/neuralkg
공식 코드NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
https://github.com/zjunlp/MKG_Analogy
공식 코드https://github.com/zjunlp/PromptKG/tree/main/deltaKG
공식 코드https://github.com/zjunlp/knn-kg
공식 코드Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
https://github.com/zjunlp/promptkg
공식 코드LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/zknus/hamiltonian-gnn
공식 코드Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
https://gitlab.com/chen00217/bert_subsumption
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://gitlab.com/fildne/fildne
공식 코드FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
https://gitlab.com/patryk.preisner/mkga
공식 코드Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573
공식 코드Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
https://worksheets.codalab.org/worksheets/0xfcace41fdeec45f3bc6ddf31107b829f
공식 코드https://github.com/18552433513/idontlikecode
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
https://github.com/2023-MindSpore-4/Code14/tree/main/SPADE
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
https://github.com/2myeonggyu/Graph-Embedding
https://github.com/404nofound/gat
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/AlexMRuch/Can-x2vec-Save-Lives
https://github.com/Alishaba/pykeen-snapshot-ensembles
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
https://github.com/Anak2016/GAT
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Anou9531/GAT
https://github.com/AryaKoureshi/Magnocellular-Parvocellular-Coactivation-Task-in-MATLAB
Modelling Brain Connectivity Networks by Graph Embedding for Dyslexia Diagnosis
https://github.com/AutoML-4Paradigm/AutoSF
Bilinear Scoring Function Search for Knowledge Graph Learning
https://github.com/AutoML-4Paradigm/AutoSF/tree/AutoSF-OGB
Bilinear Scoring Function Search for Knowledge Graph Learning
https://github.com/AutoML-4Paradigm/S2S
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
https://github.com/AutoML-Research/S2S
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/BIG-S2/keras-gnm
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.