Papers with Code 아카이브
Link Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.
<span style="color:grey; opacity: 0.6">( Image credit: Inductive Representation Learning on Large Graphs )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ShwetaBaranwal/Transformer
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/Slimnios/SaGess
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
https://github.com/SmartDataAnalytics/PyKEEN
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
https://github.com/SpikeKing/My-Bert
https://github.com/Starlien95/GraphPrompt
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
https://github.com/StatsDLMathsRecomSys/Self-attention-with-Functional-Time-Representation-Learning
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
https://github.com/StoneGH/bert
https://github.com/Sujit-O/pykg2vec
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
https://github.com/Synerise/booking-challenge
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
https://github.com/THUDM/GATNE
Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/THUDM/MCNS
Understanding Negative Sampling in Graph Representation Learning
https://github.com/THUNLP-MT/THUMT
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/TatsuyaShirakawa/poincare-embedding
Poincaré Embeddings for Learning Hierarchical Representations
https://github.com/TeamLab/bert-gcn-for-paper-citation
https://github.com/The-AI-Summer/self_attention
https://github.com/TheJacksonLaboratory/N2V
https://github.com/TheTensorDude/transformer-tf
https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need
https://github.com/Tigter/ogblwikikg2-RotPro
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
https://github.com/Tioz90/DiffPool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Tioz90/GCN
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/TonyX19/bert_hyperpartisan
https://github.com/Trent-tangtao/embedding
https://github.com/TyngJiunKuo/deep-learning-project
https://github.com/UdbhavPrasad072300/Transformer-Implementation
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
https://github.com/UdbhavPrasad072300/Transformer-Implementations
https://github.com/UlionTse/mlgb
https://github.com/Userepic/KGAT-pytorch-master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.