https://github.com/liuxinkai94/Graph-embedding
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Papers with Code 아카이브
Graph Representation Learning
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The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/logic-reasoning/Paper-Reading
https://github.com/long-9621/splinecnn
https://github.com/lsj2408/GraphNorm
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
https://github.com/lt610/GraphSaint
https://github.com/lucapantea/LightGCN
https://github.com/luckytiger123/dropmessage
DropMessage: Unifying Random Dropping for Graph Neural Networks
https://github.com/lukecavabarrett/pna
https://github.com/luxtu/OCTA-graph
https://github.com/marina-sp/reinform
https://github.com/marlin-codes/HTGN
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/maysambehmanesh/SGCL
https://github.com/mengliu1998/DeeperGNN
https://github.com/mfmceneaney/Lambda-GNNs
https://github.com/microsoft/Graphormer
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/migalkin/NodePiece
https://github.com/migalkin/NodePiece/tree/main/ogb
https://github.com/mindspore-ai/models/tree/master/research/nlp/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/miralab-ustc/qe-cone
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/mklissa/phi_gcn
https://github.com/mniepert/mmkb
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
https://github.com/mtbarta/hyperbolic
https://github.com/ncfrey/litmatter
https://github.com/njuhtc/LEDG
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/nslab-cuk/community-aware-graph-transformer
https://github.com/nyushcs/gaugllm
Large-Scale Representation Learning on Graphs via Bootstrapping
https://github.com/pfnet-research/treewidth-prediction
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/qianghuangwhu/benchtemp
https://github.com/qslim/PDF
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
https://github.com/rucaibox/safe
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/safreita1/malnet-image
https://github.com/shiy-li/mqe
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
https://github.com/shuo-git/GraphGAN
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/simonepiaggesi/dine
https://github.com/snap-stanford/ConE
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.