https://github.com/PKU-ML/canonicalization
공식 코드A Canonicalization Perspective on Invariant and Equivariant Learning
Papers with Code 아카이브
Graph Embedding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/PaddlePaddle/Research
공식 코드https://github.com/PaddlePaddle/models
공식 코드https://github.com/PetarV-/GAT
공식 코드https://github.com/Pzoom522/ProcrustEs-KGE
공식 코드Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
https://github.com/RManLuo/CP-GNN
공식 코드Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
https://github.com/RingBDStack/AGE
공식 코드A Robust and Generalized Framework for Adversarial Graph Embedding
https://github.com/RingBDStack/HAN-AT-based-KBC
공식 코드Modeling relation paths for knowledge base completion via joint adversarial training
https://github.com/RuYunW/ADG-Seq2Seq
공식 코드https://github.com/SabanciParallelComputing/GOSH
공식 코드https://github.com/Shen-Lab/Drug-Combo-Generator
공식 코드Network-principled deep generative models for designing drug combinations as graph sets
https://github.com/SkBlaz/DNR
공식 코드Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
https://github.com/SkBlaz/SGE
공식 코드https://github.com/SmartDataAnalytics/LiteralE
공식 코드https://github.com/Srinivas-R/AKBC-2017-Paper-14
공식 코드Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
공식 코드https://github.com/Sujit-O/pykg2vec
공식 코드https://github.com/SwiftieH/GFAttack
공식 코드A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
https://github.com/Synerise/booking-challenge
공식 코드https://github.com/Synerise/cleora
공식 코드Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/THUDM/cogdl
공식 코드https://github.com/Thinklab-SJTU/ThinkMatch
공식 코드Learning Combinatorial Embedding Networks for Deep Graph Matching
https://github.com/TimDettmers/ConvE
공식 코드https://github.com/Tommoo/FinslerMDS
공식 코드https://github.com/Wentao-Xu/SEEK
공식 코드https://github.com/Westlake-AI/openmixup
공식 코드GenURL: A General Framework for Unsupervised Representation Learning
https://github.com/YueLiu/NeuralTripleTranslation
공식 코드Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
https://github.com/ZINUX1998/DyCSC
공식 코드DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure
https://github.com/Zening-Li/PrivGE
공식 코드Privacy-Preserving Graph Embedding based on Local Differential Privacy
https://github.com/ZihengZZH/OntoEA
공식 코드OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
https://github.com/ZiweiXU/LENSR
공식 코드https://github.com/abachaa/evaluationmetrics-acl23
공식 코드An Investigation of Evaluation Metrics for Automated Medical Note Generation
https://github.com/abdouskamel/hmge
공식 코드Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
https://github.com/abdouskamel/hyper-mge
공식 코드A Geometric Perspective for High-Dimensional Multiplex Graphs
https://github.com/adaruna3/continual-kge
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.