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Link Prediction

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하위 태스크: 6
평가표: 79
보존된 평가 기록: 497

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/Kevin-Vora/bert-embedding-gluonnlp-edit-

Attention Is All You Need

https://github.com/Khalid-Jamal/annotated-transformer-Arabic

Attention Is All You Need

https://github.com/Ki-Seki/KGCN-pytorch-updated

Knowledge Graph Convolutional Networks for Recommender Systems

https://github.com/Kirankumaraswamy/Transformers

Attention Is All You Need

https://github.com/KrishnaTarun/Deep-Learning-Lab

Attention Is All You Need

https://github.com/Kristof-Neys/GAT_Neo4j_DGL

Graph Attention Networks

https://github.com/Kyubyong/transformer

Attention Is All You Need

https://github.com/LB0828/conve_reproduce

Convolutional 2D Knowledge Graph Embeddings

https://github.com/LJeub/Local2Global

Local2Global: Scaling global representation learning on graphs via local training

https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/LeiCaiwsu/LGLP

Line Graph Neural Networks for Link Prediction

https://github.com/Lemoners/Attention-Is-All-You-Need

Attention Is All You Need

https://github.com/LeoWood/bert-horovod

Attention Is All You Need

https://github.com/LiamMaclean216/Pytorch-Chatbot

Attention Is All You Need

https://github.com/LiamMaclean216/Pytorch-Transfomer

Attention Is All You Need

https://github.com/LinXueyuanStdio/EchoEA

Relational Reflection Entity Alignment

https://github.com/Linear95/DetGP

Dynamic Embedding on Textual Networks via a Gaussian Process

https://github.com/LoveYang/bert_test

Attention Is All You Need

https://github.com/Lsdefine/attention-is-all-you-need-keras

Attention Is All You Need

https://github.com/LunaBlack/KGAT-pytorch

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/M-Lampert/DyGLib

Towards Better Dynamic Graph Learning: New Architecture and Unified Library

https://github.com/MCLYang/RhythmGAN_pytorch

Attention Is All You Need

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

Attention Is All You Need

https://github.com/MS-Mind/MS-Code-01/tree/main/rotate

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/MaZhiyuanBUAA/bert-tf1.4.0

Attention Is All You Need

https://github.com/ManasRMohanty/DS5500-capstone

KG-BERT: BERT for Knowledge Graph Completion

https://github.com/Maple728/transformer

Attention Is All You Need

https://github.com/MarcBS/keras

Attention Is All You Need

https://github.com/MashaKubyshina/learning_ML

Attention Is All You Need

https://github.com/Matthewdowney18/Transformer_Dialogue

Attention Is All You Need

https://github.com/MaxenceGiraud/TensorTemporalKG

Tucker decomposition-based Temporal Knowledge Graph Completion

https://github.com/Maz101/Bert

Attention Is All You Need

https://github.com/Maz101/pretrainingdataset

Attention Is All You Need

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

https://github.com/MichSchli/RelationPrediction

Modeling Relational Data with Graph Convolutional Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.