https://github.com/wzh-nlp/ookg
공식 코드Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
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
- 497
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/xgfs/verse
공식 코드https://github.com/xhuang2016/splpg
공식 코드Demystifying Distributed Training of Graph Neural Networks for Link Prediction
https://github.com/xiangwang1223/knowledge_graph_attention_network
공식 코드https://github.com/xiangwang1223/neural_graph_collaborative_filtering
공식 코드https://github.com/xiangyue9607/BioNEV
공식 코드Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
https://github.com/xiaohui-victor-li/FinDKG
공식 코드https://github.com/xiaojuantang/rule
공식 코드https://github.com/xiaomingaaa/biokdn
공식 코드Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
https://github.com/xinxingwu-uk/dgae
공식 코드Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction
https://github.com/xiongbo010/neste
공식 코드NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
https://github.com/xiongbo010/qgcn
공식 코드https://github.com/xiongbo010/shrinke
공식 코드https://github.com/xjiaf/tetgn
공식 코드https://github.com/xt-55/GTRL
공식 코드GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method
https://github.com/xtra-computing/awesome-literature-ilogs
공식 코드A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
https://github.com/xuChenSJTU/SAT-master-online
공식 코드https://github.com/xueqic/topoedge
공식 코드Edge Classification on Graphs: New Directions in Topological Imbalance
https://github.com/xwhan/DeepPath
공식 코드DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/xyjigsaw/kiscovery
공식 코드Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
https://github.com/y-zhang-hf/NeighborhoodSmoothing
공식 코드Estimating network edge probabilities by neighborhood smoothing
https://github.com/yangzez/ood-link-prediction-generalization-mpnn
공식 코드OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
https://github.com/yao8839836/edge_mae
공식 코드Friend Ranking in Online Games via Pre-training Edge Transformers
https://github.com/yao8839836/kg-bert
공식 코드https://github.com/yecchen/secogd
공식 코드https://github.com/yh-yao/FedRule
공식 코드FedRule: Federated Rule Recommendation System with Graph Neural Networks
https://github.com/yifan-h/pge
공식 코드https://github.com/yin-gz/nary-inductive-subgraph
공식 코드Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning
https://github.com/yizhilll/transher
공식 코드TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
https://github.com/ykrmm/benchmarktw
공식 코드Temporal receptive field in dynamic graph learning: A comprehensive analysis
https://github.com/ykrmm/slate
공식 코드https://github.com/yongchao-liu/gipa
공식 코드GIPA: General Information Propagation Algorithm for Graph Learning
https://github.com/young0313/hap
공식 코드https://github.com/yuanyehome/palt
공식 코드PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
https://github.com/yule-BUAA/R-HGNN
공식 코드Heterogeneous Graph Representation Learning with Relation Awareness
https://github.com/yule-buaa/dyglib
공식 코드Towards Better Dynamic Graph Learning: New Architecture and Unified Library
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.