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/taokz/FedR

FedE: Embedding Knowledge Graphs in Federated Setting

https://github.com/tatp22/pytorch-fast-GAT

Graph Attention Networks

https://github.com/tensorflow/neural-structured-learning/tree/master/research/kg_hyp_emb

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

https://github.com/tgeral68/HyperbolicGraphAndGMM

From Node Embedding To Community Embedding : A Hyperbolic Approach

https://github.com/thiviyanT/torch-rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/thu-keg/eakit

Convolutional 2D Knowledge Graph Embeddings

https://github.com/thudm/graphmae2

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/tkipf/gae

Modeling Relational Data with Graph Convolutional Networks

https://github.com/tlmakinen/GAT-noise

Graph Attention Networks

https://github.com/tomdbar/eco-dqn

Learning Combinatorial Optimization Algorithms over Graphs

https://github.com/toooooodo/rgcn-linkprediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/tranhungnghiep/AnalyzingKGEmbeddings

MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction

https://github.com/tutelalabs/tutela-app

Fast Sequence-Based Embedding with Diffusion Graphs

https://github.com/twktheainur/kbc

Canonical Tensor Decomposition for Knowledge Base Completion

https://github.com/uma-pi1/dist-kge

Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings

https://github.com/uma-pi1/kge-iclr20

Convolutional 2D Knowledge Graph Embeddings

https://github.com/usherwang02/SemanticMatchingEnergy-Theano

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

https://github.com/vailatuts/SSFG-regularization

Graph Attention Networks

https://github.com/vermaMachineLearning/Universal-Graph-Embedding-Neural-Network

Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning

https://github.com/vincentschen/limited-label-scene-graphs

Scene Graph Prediction with Limited Labels

https://github.com/vwz/topolstm

From Node Embedding To Community Embedding

https://github.com/wan2000/mc-mot

Graph Attention Networks

https://github.com/webnlg/webnlg-text-to-triples

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

https://github.com/weiyangfb/PyTorchSparseGAT

Graph Attention Networks

https://github.com/wh0330/CAG_VisDial

Iterative Context-Aware Graph Inference for Visual Dialog

https://github.com/whut2962575697/gat_sementic_segmentation

Graph Attention Networks

https://github.com/wliu88/AttentivePathRanking

Path Ranking with Attention to Type Hierarchies

https://github.com/woodcutter1998/ogb-grail-mod

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/xiaojunxu/dnn-binary-code-similarity

Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

https://github.com/xingsumq/us-defake

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented

Modeling Relational Data with Graph Convolutional Networks

https://github.com/ydzhang-stormstout/HAT

Graph Attention Networks

https://github.com/youngandbin/pfotgnrec

Inductive Representation Learning on Temporal Graphs

https://github.com/yunchengwang/graph-representation-learning

Graph Representation Learning: A Survey

https://github.com/yunshengb/UGraphEmb

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

https://github.com/yururyuru00/GAT_pyG

Graph Attention Networks

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