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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/agoodweathercc/EmbeddingEval

Graph Embedding Techniques, Applications, and Performance: A Survey

https://github.com/ak24watch/Graph-Deeplearning-GAT

Graph Attention Networks

https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network

Graph Attention Networks

https://github.com/alexOarga/haiku-geometric

Graph Attention Networks

https://github.com/alexrenz/adaps

NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access

https://github.com/allenai/commonsense-kg-completion

Commonsense Knowledge Base Completion with Structural and Semantic Context

https://github.com/anilakash/indkgc

Modeling Relational Data with Graph Convolutional Networks

https://github.com/anish-lu-yihe/SVRT-by-GAT

Graph Attention Networks

https://github.com/anonymous-dense-submission/DensE

DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy

https://github.com/ant-research/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE

PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

https://github.com/ant-research/parameter_inference_efficient_pie

PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning

https://github.com/apoorvumang/CronKGQA

Tensor Decompositions for temporal knowledge base completion

https://github.com/arangoml/fastgraphml

Graph Attention Networks

https://github.com/ariellubonja/ligra

One-Hot Graph Encoder Embedding

https://github.com/automl-research/nscaching

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/automl-research/red-gnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/awslabs/dgl-ke

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

https://github.com/basiralab/HADA

Adversarially Regularized Graph Autoencoder for Graph Embedding

https://github.com/basiralab/LG-DADA

Adversarially Regularized Graph Autoencoder for Graph Embedding

https://github.com/basiralab/RG-Select

Graph Attention Networks

https://github.com/basiralab/Reproducible-Generative-Learning

Graph Attention Networks

https://github.com/benedekrozemberczki/AttentionWalk

Watch Your Step: Learning Node Embeddings via Graph Attention

https://github.com/benedekrozemberczki/SEAL-CI

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

https://github.com/benedekrozemberczki/Splitter

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

https://github.com/benedekrozemberczki/graph2vec

graph2vec: Learning Distributed Representations of Graphs

https://github.com/berlincho/RGCN-pytorch

Modeling Relational Data with Graph Convolutional Networks

https://github.com/bi-graph/emgraph

Convolutional 2D Knowledge Graph Embeddings

https://github.com/bio-ontology-research-group/EL2Box_embedding

EL Embeddings: Geometric construction of models for the Description Logic EL ++

https://github.com/blueberryc/pyGAT

Graph Attention Networks

https://github.com/bowu/GraphSearch

Graph Attention Networks

https://github.com/calciver/Graph-Attention-Networks

Graph Attention Networks

https://github.com/canlinzhang/siailp

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/cdhx/KBGAN_for_RotatE

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

https://github.com/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/cheungdaven/QuatE

Quaternion Knowledge Graph Embeddings

https://github.com/compnet/pang

graph2vec: Learning Distributed Representations of Graphs

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