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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/dice-group/Convolutional-Hypercomplex-Embeddings-for-Link-Prediction

공식 코드

Convolutional Hypercomplex Embeddings for Link Prediction

https://github.com/dice-group/OOV-In-Link-Prediction

공식 코드

Out-of-Vocabulary Entities in Link Prediction

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

공식 코드

A Physical Embedding Model for Knowledge Graphs

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

공식 코드

Kronecker Decomposition for Knowledge Graph Embeddings

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

공식 코드

Universal Knowledge Graph Embeddings

https://github.com/didi/hetsann

공식 코드

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

https://github.com/digitalTranshumant/WikiRecNet-ComplexRec2020

공식 코드

Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning

https://github.com/dihuang0220/GraphEnsembleLearning

공식 코드

Graph Representation Ensemble Learning

https://github.com/dishashur/log-pagerank

공식 코드

A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings

https://github.com/dmirlab-group/mgnn

공식 코드

Motif Graph Neural Network

https://github.com/dodo47/SpikE

공식 코드

SpikE: spike-based embeddings for multi-relational graph data

https://github.com/dodo47/cyberml

공식 코드

An energy-based model for neuro-symbolic reasoning on knowledge graphs

https://github.com/drewjin/GSIFN

공식 코드

GSIFN: A Graph-Structured and Interlaced-Masked Multimodal Transformer-based Fusion Network for Multimodal Sentiment Analysis

https://github.com/dsaatusu/mooper_grade_prediction

공식 코드

Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

https://github.com/dwslab/jRDF2Vec

공식 코드

RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

https://github.com/eBay/xFraud

공식 코드

xFraud: Explainable Fraud Transaction Detection

https://github.com/eXascaleInfolab/daor

공식 코드

Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection

https://github.com/edhirst/clusteralgebrasml

공식 코드

Cluster Algebras: Network Science and Machine Learning

https://github.com/edwarddavis1/valid_conformal_for_dynamic_gnn

공식 코드

Valid Conformal Prediction for Dynamic GNNs

https://github.com/ellenzhuwang/lakermap

공식 코드

Contextualized Structural Self-supervised Learning for Ontology Matching

https://github.com/eric991005/multitask-stockformer

공식 코드

Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks

https://github.com/eric991005/stockformer

공식 코드

Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks

https://github.com/facebookresearch/fastText

공식 코드

Fast Linear Model for Knowledge Graph Embeddings

https://github.com/facebookresearch/kbc

공식 코드

Canonical Tensor Decomposition for Knowledge Base Completion

https://github.com/facebookresearch/ssl-relation-prediction

공식 코드

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

https://github.com/facebookresearch/tkbc

공식 코드

Tensor Decompositions for temporal knowledge base completion

https://github.com/fahadsohrab/gessvdd

공식 코드

Graph-Embedded Subspace Support Vector Data Description

https://github.com/fanourakis/experimental-review-ea

공식 코드

Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review

https://github.com/franklinnwren/ds-age

공식 코드

https://github.com/fudancisl/freegem

공식 코드

Parameter-free Dynamic Graph Embedding for Link Prediction

https://github.com/fzx-oss/wwls

공식 코드

Wasserstein Graph Distance Based on $L_1$-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees

https://github.com/gdmnl/scara-ppr

공식 코드

SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

https://github.com/george-yuanji-wang/gan-tat

공식 코드

GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification

https://github.com/georgeguo-cn/lgmrec

공식 코드

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

https://github.com/georgemlp/canonicalization

공식 코드

A Canonicalization Perspective on Invariant and Equivariant Learning

https://github.com/georgemlp/laplacian-canonization

공식 코드

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

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