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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/nishnik/poincare_embeddings

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/nmvdlei/PPMI_MAGNN

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

https://github.com/nnaakkaaii/g2-MLP

Graph Attention Networks

https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2

Graph Attention Networks

https://github.com/olekscode/Power2TheWiki

Graph Embedding Techniques, Applications, and Performance: A Survey

https://github.com/opengsl/opengsl

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

https://github.com/parkererickson/crunchBaseGraph

Modeling Relational Data with Graph Convolutional Networks

https://github.com/paulmorio/geo2dr

graph2vec: Learning Distributed Representations of Graphs

https://github.com/pengsl-lab/mssl

Graph Attention Networks

https://github.com/pengsl-lab/mssl2drug

Graph Attention Networks

https://github.com/pouyapez/multim-kb-embeddings

Embedding Multimodal Relational Data for Knowledge Base Completion

https://github.com/predict-idlab/RR-GCN

Modeling Relational Data with Graph Convolutional Networks

https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/kge/rotate.py

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/pykeen/ilpc2022

Composition-based Multi-Relational Graph Convolutional Networks

https://github.com/qema/orca-py

Graph Attention Networks

https://github.com/raphaelsty/mkb

Inductive Entity Representations from Text via Link Prediction

https://github.com/sadhikamalladi/RL-Coloring

Learning Combinatorial Optimization Algorithms over Graphs

https://github.com/sds-lab/rot-pooling

Revisiting Global Pooling through the Lens of Optimal Transport

https://github.com/seeyourmind/tkgelib

HittER: Hierarchical Transformers for Knowledge Graph Embeddings

https://github.com/shenweichen/GraphEmbedding

LINE: Large-scale Information Network Embedding

https://github.com/shenweichen/GraphNeuralNetwork

Graph Attention Networks

https://github.com/shi27feng/transformers.satisfy

Graph Attention Networks

https://github.com/shijx12/kqapro_baselines

Modeling Relational Data with Graph Convolutional Networks

https://github.com/snap-stanford/ConE

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/snap-stanford/graphwave

struc2vec: Learning Node Representations from Structural Identity

https://github.com/snowkylin/gnn

Graph Attention Networks

https://github.com/snownus/COOP

Graph Attention Networks

https://github.com/soumavaghosh/graph2vec

graph2vec: Learning Distributed Representations of Graphs

https://github.com/stanfordnlp/cocoa

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

https://github.com/subercui/pyGConvAT

Graph Attention Networks

https://github.com/sunhwan-lee/recommender_system

Recommendation Through Mixtures of Heterogeneous Item Relationships

https://github.com/sunlab-osu/BioNEV

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

https://github.com/susurrant/flow-imputation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/susurrant/spatial-interaction-modeling

Modeling Relational Data with Graph Convolutional Networks

https://github.com/svjan5/gnns-for-nlp

Neural Graph Embedding Methods for Natural Language Processing

https://github.com/taishan1994/pytorch_gat

Graph Attention Networks

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