Papers with Code 아카이브
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.
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https://github.com/abojchevski/graph2gauss
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
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https://github.com/adsieg/Multi_Text_Classification
Poincaré Embeddings for Learning Hierarchical Representations
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DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
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https://github.com/aida-ugent/NRLBenchmark
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akanyaani/minGPTF
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https://github.com/alibabasglab/frcrn
https://github.com/alijazayeri/tempowork
Frequent Pattern Mining in Continuous-time Temporal Networks
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
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https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/alisadeghian/DRUM
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
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Commonsense Knowledge Base Completion with Structural and Semantic Context
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https://github.com/amazon-research/panrep
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https://github.com/anish-lu-yihe/SVRT-by-GAT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.