https://github.com/TeamW-P/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/Tigter/ogblwikikg2-RotPro
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
https://github.com/Tioz90/DiffPool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Tioz90/GCN
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Trent-tangtao/embedding
https://github.com/VoVAllen/diffpool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/WeidongLi-KG/KBGAN_PyTorch-v0.4.1
https://github.com/YuxiangRen/Heterogeneous-Deep-Graph-Infomax
https://github.com/ZFT-CQU/DSDL
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
https://github.com/a19hu/Social_media_export_chat
https://github.com/adymaharana/DeepPath_PyTorch
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/aidos-lab/ECT
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py
https://github.com/alge24/eigenpooling
https://github.com/anilakash/indkgc
Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/anonymous-dense-submission/DensE
https://github.com/anonymous-sourcecode/DifNN
https://github.com/ansonb/deft
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/ant-research/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
https://github.com/ant-research/parameter_inference_efficient_pie
https://github.com/asarigun/GraphMixerNetworks
https://github.com/asrtoufa/wikispeedia-paths-dual-hypergraph-features
https://github.com/automl-research/nscaching
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
https://github.com/automl-research/red-gnn
https://github.com/awslabs/dgl-ke
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/basiralab/RG-Select
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/basiralab/Reproducible-Generative-Learning
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/basiralab/falcon
https://github.com/basiralab/reproduciblefedgnn
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/benedekrozemberczki/pytorch_geometric_temporal
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/bi-graph/emgraph
Probability Calibration for Knowledge Graph Embedding Models
https://github.com/bio-ontology-research-group/EL2Box_embedding
EL Embeddings: Geometric construction of models for the Description Logic EL ++
https://github.com/borgwardtlab/pst
Structure-Aware Transformer for Graph Representation Learning
https://github.com/calciver/Graph-Isomorphism-Networks
https://github.com/canlinzhang/siailp
https://github.com/cdhx/KBGAN_for_RotatE
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.