Papers with Code 아카이브
Link Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-arxiv/unimp_appnp_vnode_smooth
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-proteins/unimp_cross_feat
https://github.com/PaddlePaddle/PGL/tree/master/examples/GaAN
https://github.com/PaddlePaddle/PGL/tree/master/ogb_examples/nodeproppred/ogbn-arxiv
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/PasqualeAuriemma/GCN-DIFFPOOL
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Pezhin/best_yelp_review_project_ever
https://github.com/PreferredAI/cornac
https://github.com/PrideLee/sentiment-analysis
https://github.com/PumpkinYing/GAT
https://github.com/PurdueMINDS/Equivalence
On the Equivalence between Positional Node Embeddings and Structural Graph Representations
https://github.com/QCOIN-LAB/E2ECPE
End-to-end Emotion-Cause Pair Extraction via Learning to Link
https://github.com/QustKcz/BioNEV
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
https://github.com/QustKcz/relational-GCN
https://github.com/Rami97rgb/French-to-English-Translator
https://github.com/Remiphilius/PoemesProfonds
https://github.com/RenXiangyuan/tf_bert
https://github.com/RexYing/diffpool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/RexYing/graph-pooling
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/RingBDStack/DP-CroSUE/tree/main/DP-CroSUE-mindspore/
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
https://github.com/Robotmurlock/Deepwalk-and-Node2vec
https://github.com/Rudedaisy/attention-is-all-you-need-pytorch
https://github.com/Rufaim/EvolveGCN
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/Ruiqi-Hu/ARGA
Adversarially Regularized Graph Autoencoder for Graph Embedding
https://github.com/SCismycat/bert_code_view
https://github.com/SY1706203/GATNE
Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/SYangDong/bert-with-frozen-code
https://github.com/Satan012/BERT
https://github.com/SeoroMin/transformer_pytorch
https://github.com/SeoroMin/transformer_pytorch_ver2
https://github.com/ShaharGottlieb/GNNExplainer_DGL
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/Shasvat-Desai/Shasvat-Desai
https://github.com/Shinya-Kouda/kgc
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.