Papers with Code 아카이브

Graph Embedding

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하위 태스크: 5
평가표: 1
보존된 평가 기록: 1

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

<span style="color:grey; opacity: 0.6">( Image credit: GAT )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

https://github.com/MichSchli/RelationPrediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/Microsoft/gated-graph-neural-network-samples

Modeling Relational Data with Graph Convolutional Networks

https://github.com/Mind23-2/MindCode-4/tree/main/gat

Graph Attention Networks

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-scientific-2/code-4/tree/main/GR-HSTU

Graph Attention Networks

https://github.com/MurtyShikhar/KBI

Joint Matrix-Tensor Factorization for Knowledge Base Inference

https://github.com/NIVA-Knowledge-Graph/NIVAUC

Knowledge Graph Embedding for Ecotoxicological Effect Prediction

https://github.com/Negus25/GAT-Mindspore

Graph Attention Networks

https://github.com/PumpkinYing/GAT

Graph Attention Networks

https://github.com/QustKcz/BioNEV

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

https://github.com/QustKcz/relational-GCN

Modeling Relational Data with Graph Convolutional Networks

https://github.com/RingBDStack/DGGAN

Adversarial Directed Graph Embedding

https://github.com/Ruiqi-Hu/ARGA

Adversarially Regularized Graph Autoencoder for Graph Embedding

https://github.com/SY1706203/GATNE

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/SmartDataAnalytics/PyKEEN

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

https://github.com/StatsDLMathsRecomSys/Self-attention-with-Functional-Time-Representation-Learning

Inductive Representation Learning on Temporal Graphs

https://github.com/THUDM/GATNE

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/TatsuyaShirakawa/poincare-embedding

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/Tiger101010/DAEGC

Attributed Graph Clustering: A Deep Attentional Embedding Approach

https://github.com/Tigter/ogblwikikg2-RotPro

Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

https://github.com/TyngJiunKuo/deep-learning-project

Graph Attention Networks

https://github.com/Wang-Yu-Qing/EGES

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

https://github.com/WantingZhao/my_GAT

Graph Attention Networks

https://github.com/WeidongLi-KG/KBGAN_PyTorch-v0.4.1

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

https://github.com/YechengLyu/TreeRNN

TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning

https://github.com/Yindong-Zhang/myGAT

Graph Attention Networks

https://github.com/YunseobShin/wiki_GAT

Graph Attention Networks

https://github.com/ZimpleX/gcn-ipdps19

Accurate, Efficient and Scalable Graph Embedding

https://github.com/a19hu/Social_media_export_chat

Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

https://github.com/abhilash1910/Deep-Graph-Learning

LINE: Large-scale Information Network Embedding

https://github.com/abhilash1910/GraphAttentionNetworks

Graph Attention Networks

https://github.com/abhinavnagpal/KNOWLEDGE-GRAPH-PAPERS

Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text

https://github.com/adsieg/Multi_Text_Classification

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/adymaharana/DeepPath_PyTorch

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.