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Graph Representation Learning
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The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gmum/umwpl2021
https://github.com/gospodima/extended-simgnn
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/graphcore-research/bess-kge
BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
https://github.com/graphcore/ogb-lsc-pcqm4mv2
https://github.com/guillaumejaume/tuto-dl-on-graphs
https://github.com/guixiangyu1/KGE-HAKE-master
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/hamed1375/exphormer
https://github.com/hkuds/recdiff
https://github.com/hsientzucheng/MKR.PyTorch
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
https://github.com/huangwb/AS-GCN
Adaptive Sampling Towards Fast Graph Representation Learning
https://github.com/hughxiouge/CompoundE3D
Knowledge Graph Embedding with 3D Compound Geometric Transformations
https://github.com/hwwang55/KGCN
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
https://github.com/hyeamykim/GCN-related-works
https://github.com/hyunjin72/GraphGAN-PyTorch
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/igorsterner/commute-opt-gnn
https://github.com/jaemuzzin/plagnn
https://github.com/jiean001/models_m/tree/main/hake
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/jiean001/models_m/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/johannesmessner/boxte
https://github.com/johnny12150/DySAT_python3
Dynamic Graph Representation Learning via Self-Attention Networks
https://github.com/josephjwilson/cayley_graph_propagation
https://github.com/juellsprott/graphair-reproducibility
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
https://github.com/junxia97/progcl
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
https://github.com/k4my4r/ECG-Classification
https://github.com/karolismart/dropgnn
https://github.com/kingcong/models/tree/main/hake
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/knowlab/bi-weekly-paper-presentation
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/kzkadc/poly-nets
https://github.com/lars-research/red-gnn
https://github.com/leojklarner/gauche
https://github.com/lfhase/gala
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
https://github.com/lhrlab/dhge
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
https://github.com/linmengsysu/IsoNN
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
https://github.com/linusbao/MoSE
https://github.com/liutongyang/GraphGAN-pytorch
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.