Papers with Code 아카이브

Graph Embedding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/cui0523/Code6/tree/main/rotate

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/cynricfu/MAGNN

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

https://github.com/dair-iitd/kbc-baseline

Knowledge Base Completion: Baseline strikes back (Again)

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/danifuertes/top_transformer

Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning

https://github.com/daniilidis-group/all-graphs-lead-to-rome

All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks

https://github.com/davendw49/gakg

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/dellixx/TransERR

TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation

https://github.com/dglai/wsdm2022-challenge

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dice-group/hybridfc

Convolutional Complex Knowledge Graph Embeddings

https://github.com/dinobby/hypemo

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/GATNE-T

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/dmlc/dgl/tree/master/examples/pytorch/compGCN

Composition-based Multi-Relational Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/eges

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

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsaint

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat

Graph Representation Learning via Hard and Channel-Wise Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/doujiang-zheng/dynamic-preference-structure

Inductive Representation Learning on Temporal Graphs

https://github.com/drewwilimitis/hyperbolic-learning

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/drewwilimitis/poincare

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification

A Simple Baseline Algorithm for Graph Classification

https://github.com/edouardpineau/Using-Laplacian-Spectrum-as-Graph-Feature-Representation

A Simple Baseline Algorithm for Graph Classification

https://github.com/esraabil/NECL

Network Embedding: on Compression and Learning

https://github.com/facebookresearch/PyTorch-BigGraph

PyTorch-BigGraph: A Large-scale Graph Embedding System

https://github.com/facebookresearch/poincare-embeddings

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/fanzhenliu/dagad

Graph Attention Networks

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