https://github.com/DeMix2023/Demix
공식 코드Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Papers with Code 아카이브
Link Prediction
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- 하위 태스크
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- 평가표
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- 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/DeepGraphLearning/KnowledgeGraphEmbedding
공식 코드RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/DeepGraphLearning/NBFNet
공식 코드Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
https://github.com/DeepGraphLearning/ULTRA
공식 코드https://github.com/DimBer/ASE-project
공식 코드Node Embedding with Adaptive Similarities for Scalable Learning over Graphs
https://github.com/DimitrisAlivas/StarQE
공식 코드https://github.com/Elric2718/Bayes_Embedding
공식 코드https://github.com/Erican-Ji/CSGDN
공식 코드CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
https://github.com/FelixFrohnertDB/arxiv_nlp
공식 코드Discovering emergent connections in quantum physics research via dynamic word embeddings
https://github.com/GemsLab/LinkWaldo
공식 코드A Hidden Challenge of Link Prediction: Which Pairs to Check?
https://github.com/GhadeerAbuoda/LinkPrediction
공식 코드https://github.com/GraphPKU/NeuralCommonNeighbor
공식 코드https://github.com/HKUST-KnowComp/FKGE
공식 코드https://github.com/HKUST-KnowComp/HPHG
공식 코드https://github.com/HKUST-KnowComp/NeuralSubgraphCounting
공식 코드https://github.com/HKUST-KnowComp/vertex-reinforced-random-walk
공식 코드https://github.com/HassounLab/ELP
공식 코드https://github.com/HipGraph/Force2Vec
공식 코드https://github.com/Hippopotamus0308/SSTKG
공식 코드https://github.com/Hope-Rita/RepeatMixer
공식 코드https://github.com/IBM/AMLSim
공식 코드EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/IBM/EvolveGCN
공식 코드EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/IBM/TM-GCN
공식 코드Dynamic Graph Convolutional Networks Using the Tensor M-Product
https://github.com/INDElab/rgvae
공식 코드Knowledge Generation -- Variational Bayes on Knowledge Graphs
https://github.com/INK-USC/DArtNet
공식 코드Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
https://github.com/INK-USC/RE-Net
공식 코드Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
https://github.com/JD-AI-Research-Silicon-Valley/SACN
공식 코드End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
https://github.com/JeanKaddour/WASAM
공식 코드https://github.com/Jeffrey-Sardina/TWIG-I
공식 코드https://github.com/Jeffrey-Sardina/TWIG-TWM-dev
공식 코드https://github.com/JiaweiSheng/FAAN
공식 코드Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
https://github.com/JiaxuanYou/P-GNN
공식 코드https://github.com/JimLiu96/basConv
공식 코드https://github.com/JinheonBaek/GEN
공식 코드Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
https://github.com/Juintin/GAE-AT
공식 코드On Generalization of Graph Autoencoders with Adversarial Training
https://github.com/KashinYana/link-prediction
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.