https://github.com/zknus/hamiltonian-gnn
공식 코드Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Papers with Code 아카이브
Link Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 79
- 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/zwangeo/autogel
공식 코드AutoGEL: An Automated Graph Neural Network with Explicit Link Information
https://github.com/zwt233/NAFS
공식 코드NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
https://github.com/zwt233/ROD
공식 코드https://github.com/zyang-16/MCNS
공식 코드Understanding Negative Sampling in Graph Representation Learning
https://gitlab.com/agarwal.khushbu/Snomed2Vec
공식 코드https://gitlab.com/fildne/fildne
공식 코드FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
https://gitlab.com/mipl/graph-learning-network
공식 코드https://gitlab.com/tksaha/temporalnode2vec
공식 코드https://gitlab.com/wxwilcke/mrgcn
공식 코드https://github.com/04RR/SOTA-Vision
https://github.com/18552433513/idontlikecode
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/transX
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/audio_spectrogram_transformer
https://github.com/2miatran/Natural-Language-Processing
https://github.com/2myeonggyu/Graph-Embedding
https://github.com/404nofound/gat
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/AaltoPML/Rethinking-pooling-in-GNNs
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Accenture/AmpliGraph
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
https://github.com/Aditya239233/GNNExplainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/AlCorreia/FABIR
https://github.com/AlexDuvalinho/GNNE_eval
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/Alishaba/pykeen-snapshot-ensembles
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
https://github.com/Amy-1114/transformer
https://github.com/AnacletoLAB/ensmallen
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
https://github.com/Anak2016/GAT
https://github.com/AndreiMoraru123/Neural-Machine-Translation
https://github.com/Andrey885/Machine_translation_PyTorch
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/AnneDirkson/FuzzyBIO
https://github.com/Anou9531/GAT
https://github.com/ArdalanM/nlp-benchmarks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.