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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/kamigaito/icml2022
공식 코드Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
https://github.com/kargaranamir/Wide-AdGraph
공식 코드Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph
https://github.com/kg-cc/noisehgnn
공식 코드https://github.com/kgrl2021/submission-one
공식 코드A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
https://github.com/kkteru/grail
공식 코드https://github.com/kxchern/dkepool-tsa
공식 코드Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
https://github.com/lars-research/relens
공식 코드Relation-aware Ensemble Learning for Knowledge Graph Embedding
https://github.com/lblaoke/gCooL
공식 코드https://github.com/lenscloth/grpe
공식 코드https://github.com/leonresearch/city-networks
공식 코드https://github.com/leonyuhe/ctwalks
공식 코드https://github.com/lequanlin/gnn-diff
공식 코드Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
https://github.com/liangke23/awesome-knowledge-graph-reasoning
공식 코드A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
https://github.com/liangzhehan/cmod
공식 코드https://github.com/liaopeiyuan/GAL
공식 코드https://github.com/lihan97/kpgt
공식 코드KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
https://github.com/lijingzhu1/ett-ckge
공식 코드ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
https://github.com/liqimai/DSGC
공식 코드https://github.com/liseda-lab/truewalks
공식 코드Biomedical Knowledge Graph Embeddings with Negative Statements
https://github.com/llqy123/dabr
공식 코드Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
https://github.com/lrnzgiusti/can
공식 코드https://github.com/lspongebobjh/virgo_icml2023
공식 코드https://github.com/lukeyf/graphs-and-lines
공식 코드Theoretical Insights into Line Graph Transformation on Graph Learning
https://github.com/luoxiaoheics/stancerel
공식 코드https://github.com/lvrcek/NeuralLayout
공식 코드https://github.com/lzswangjian/THGRL
공식 코드https://github.com/ma-compbio/Hyper-SAGNN
공식 코드Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
https://github.com/malllabiisc/CompGCN
공식 코드Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/marcob46/mpxgat
공식 코드MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
https://github.com/marfiori/efficient-ase
공식 코드Gradient-Based Spectral Embeddings of Random Dot Product Graphs
https://github.com/masoudmo/echoglad
공식 코드https://github.com/matanat/GRACE
공식 코드Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
https://github.com/maxiaoba/GRAPE
공식 코드https://github.com/mcneela/mixed-curvature-gcn
공식 코드Product Manifold Representations for Learning on Biological Pathways
https://github.com/mcneela/mixed-curvature-pathways
공식 코드Product Manifold Representations for Learning on Biological Pathways
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.