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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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/Michiel29/ntp-release
https://github.com/Microsoft/gated-graph-neural-network-samples
https://github.com/MiloudBi/NGCF-pytorch
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindSpore-paper-code-3/code9/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/MindSpore-paper-code-3/code9/tree/main/transX
https://github.com/MindSpore-scientific-2/code-10/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/MindSpore-scientific-2/code-11/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/MindSpore-scientific-2/code-2/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/Monti03/LinkPredictionOverClusters
https://github.com/Monti03/VGAE
https://github.com/NIRVANALAN/ClusterGCN_google-reseach
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/NVIDIA/sentiment-discovery
https://github.com/NamyongPark/EvoKG
https://github.com/Negus25/GAT-Mindspore
https://github.com/Neoanarika/torchexplainer
https://github.com/Nimesh-Patel/BERT
https://github.com/Nina-Konovalova/TSP-RL-Skoltech_project
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/OctoberChang/GAMLP?fbclid=IwAR03ugAd7H0U_kQrkihBDMJ-Bxy3siwKVbnBTNkz_IVp5J25L0DFlTK0yfc
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
https://github.com/Omairss/RepresentationLearning
https://github.com/OpenXAIProject/GNNExplainer-Tutorial
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/Osobarako/alduswarrensewell
https://github.com/OweysMomenzada/Graph-Neural-Networks-for-effecient-Recommender-Systems
https://github.com/PaddlePaddle/PGL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.