https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks
A Fair Comparison of Graph Neural Networks for Graph Classification
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/Gaoyiminggithub/Graphonomy
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
https://github.com/George730/E-ResGAT
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/Graph-COM/SUREL
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
https://github.com/GraphPKU/GeoNGNN
https://github.com/GraphSAINT/GraphACT
https://github.com/HaochenLiu2000/QAP
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/HazyResearch/hgcn
https://github.com/Hunter-DDM/InvTransE-and-InvRotatE
https://github.com/JeffJeffy/CS224W-OGB-DEA-JK
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
https://github.com/KXY-PUBLIC/DiCGRL
https://github.com/KXY-PUBLIC/NASE
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
https://github.com/Kennard123661/gcc
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/LARS-research/RelEns
Relation-aware Ensemble Learning for Knowledge Graph Embedding
https://github.com/LJeub/Local2Global
Local2Global: Scaling global representation learning on graphs via local training
https://github.com/LUOyk1999/DAGformer
https://github.com/Lihaogx/DNDN
https://github.com/M-Lampert/DyGLib
https://github.com/MIRALab-USTC/GraphAKD
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
https://github.com/MIRALab-USTC/KGE-DURA
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/MS-Mind/MS-Code-01/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/MindSpore-paper-code-2/code400/tree/main/Inception/inceptionv4
https://github.com/MindSpore-paper-code-3/code9/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/MurtyShikhar/KBI
Joint Matrix-Tensor Factorization for Knowledge Base Inference
https://github.com/NIVA-Knowledge-Graph/NIVAUC
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
https://github.com/NMADALI97/French-web-domain-classification
https://github.com/Namkyeong/BGRL_Pytorch
Large-Scale Representation Learning on Graphs via Bootstrapping
https://github.com/PasqualeAuriemma/GCN-DIFFPOOL
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/PurdueMINDS/RelationalPooling
https://github.com/RexYing/diffpool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/RexYing/graph-pooling
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/RicardoL1u/GraftNet-Unofficial
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
https://github.com/Rufaim/EvolveGCN
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/SmartDataAnalytics/PyKEEN
https://github.com/Starlien95/GraphPrompt
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
https://github.com/THUDM/MCNS
Understanding Negative Sampling in Graph Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.