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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/snap-stanford/relgnn
Relational Deep Learning: Graph Representation Learning on Relational Databases
https://github.com/sseung0703/GALA_TF2.0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
https://github.com/sunnan191/evisec
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/syleeheal/aero-gnn
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
https://github.com/taokz/FedR
https://github.com/thu-keg/eakit
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/thudm/graphmae2
https://github.com/tobias-opsahl/fact-or-fiction
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/toinesayan/node-classification-and-label-dependencies
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/tonyPo/AMLSim_prep
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/tranhungnghiep/AnalyzingKGEmbeddings
https://github.com/tum-vision/intrinsic-neural-fields
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
https://github.com/tyuxie/vbpi-sibranch
Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks
https://github.com/uma-pi1/dist-kge
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
https://github.com/venomouscyanide/S3GRL_OGB
Simplifying Subgraph Representation Learning for Scalable Link Prediction
https://github.com/veritasyin/subg_acc
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
https://github.com/vermouthdky/SimTeG
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
https://github.com/wei9711/d2gcn
Understanding Negative Sampling in Graph Representation Learning
https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv
https://github.com/woodcutter1998/ogb-grail-mod
https://github.com/xiaolin-cs/bemap
Towards a Unified Framework for Fair and Stable Graph Representation Learning
https://github.com/xingsumq/us-defake
https://github.com/yangyucheng000/University/tree/main/model-2/graphormer
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/ycremar/DIG-SSL
https://github.com/ytchx1999/Graphormer/tree/main/examples/ogb
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/yule-buaa/dyglib
https://github.com/yunchengwang/graph-representation-learning
https://github.com/yuwvandy/g2gnn
https://github.com/yzhangee/NSCaching
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
https://github.com/zhliping/Deep-Learning
https://github.com/zjs123/streame
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
https://github.com/zjunlp/MKGformer
https://github.com/zjunlp/promptkgc
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/zpeng27/GMI
Graph Representation Learning via Graphical Mutual Information Maximization
https://github.com/zxlearningdeep/infomgf
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