Link Prediction
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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.
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Dynamic Graph Representation Learning via Self-Attention Networks
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Knowledge Graph Convolutional Networks for Recommender Systems
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Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
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Watch Your Step: Learning Node Embeddings via Graph Attention
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Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.