https://github.com/cgoliver/vernal
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs
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/chaoyivision/SGGpoint
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis
https://github.com/chappers/graph-differential-pooling
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/cheng0719/E-GraphSAGE_NIDS
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/cheungdaven/QuatE
https://github.com/chfhf/GCC-paddle
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/cmavro/Graph-InfoClust-GIC
https://github.com/cmavro/HeMI
https://github.com/crisbodnar/dgm
https://github.com/cui0523/Code6/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/dair-iitd/kbc-baseline
https://github.com/data-iitd/tigger
https://github.com/davendw49/gakg
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/deepgraphlearning/graphany
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
https://github.com/dellixx/TransERR
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
https://github.com/dice-group/hybridfc
https://github.com/diningphil/CGMM
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/diningphil/icgmm
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin
https://github.com/dmlc/dgl/tree/master/examples/pytorch/_deprecated/adaptive_sampling
Adaptive Sampling Towards Fast Graph Representation Learning
https://github.com/dmlc/dgl/tree/master/examples/pytorch/compGCN
Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/pytorch/dagnn
https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/dmlc/dgl/tree/master/examples/pytorch/evolveGCN
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin
https://github.com/dmlc/dgl/tree/master/examples/pytorch/grace
https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsaint
https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat
Graph Representation Learning via Hard and Channel-Wise Attention Networks
https://github.com/dmlc/dgl/tree/master/examples/pytorch/sign
https://github.com/dms-net/Attention-based-Scattering
https://github.com/dodo47/SpikE
Learning through structure: towards deep neuromorphic knowledge graph embeddings
https://github.com/dpstart/graphormer_new
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/egyptdj/graph-neural-mapping
https://github.com/facebookresearch/NARS
https://github.com/fdudsdelab/Subg-Con
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
https://github.com/fseiffarth/gnn-comparison
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.