Link Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/zpeng27/GMI
Graph Representation Learning via Graphical Mutual Information Maximization
https://github.com/zsweet/BERT_zsw
https://github.com/zxhhh97/ABot
https://github.com/zyjcs/KGE-HAE
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/zysite/post
https://github.com/zzaebok/KGCN-pytorch
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/zzz2010/ngcf-paddle2
https://gitlab.com/cedric_sanders/masterarbeit
Hierarchical Graph Representation Learning with Differentiable Pooling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.