Papers with Code 아카이브
Graph Embedding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
<span style="color:grey; opacity: 0.6">( Image credit: GAT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ahmedstohy/hybrid-pointer-networks
공식 코드Hybrid Pointer Networks for Traveling Salesman Problems Optimization
https://github.com/aicps/sg-collision-prediction
공식 코드Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction
https://github.com/aida-ugent/FIPR
공식 코드The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
https://github.com/aida-ugent/topoembedding
공식 코드https://github.com/aidos-lab/node2vec-surprises
공식 코드https://github.com/aistairc/RDF-star2Vec
공식 코드https://github.com/aistairc/kgrc-rdf-star
공식 코드https://github.com/akirato/perm-gaussiankg
공식 코드Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
https://github.com/alanjohnvarghese/TransformerG2G
공식 코드TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
https://github.com/alejgh/wikidata-edithist-refinement
공식 코드Leveraging Wikidata's edit history in knowledge graph refinement tasks
https://github.com/alexanderimanicowenrivers/Neural-Variational-Knowledge-Graphs
공식 코드Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
https://github.com/alexrenz/NuPS
공식 코드NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
공식 코드PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
https://github.com/alipay/duplex
공식 코드https://github.com/alipay/parameter_inference_efficient_pie
공식 코드https://github.com/allenhaozhu/coles
공식 코드https://github.com/amazon-science/co-with-gnns-example
공식 코드Combinatorial Optimization with Automated Graph Neural Networks
https://github.com/amberyzheng/bloomgml
공식 코드BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
https://github.com/andompesta/nodeembedding-to-communityembedding
공식 코드https://github.com/andreeaiana/rdf2vec-materialization
공식 코드https://github.com/andy20178/sgformer
공식 코드SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation
https://github.com/andyweizhao/graphs-normed-spaces
공식 코드https://github.com/anrep/house
공식 코드HousE: Knowledge Graph Embedding with Householder Parameterization
https://github.com/ansonb/hopfe
공식 코드HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
https://github.com/anwar-said/esgea
공식 코드Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
https://github.com/aoluming/IDKG
공식 코드https://github.com/apoorvumang/kgt5
공식 코드Sequence-to-Sequence Knowledge Graph Completion and Question Answering
https://github.com/april-tools/gekcs
공식 코드How to Turn Your Knowledge Graph Embeddings into Generative Models
https://github.com/arash-dehghan/EmbeddingComplexNetworks
공식 코드https://github.com/aravi11/funcGNN
공식 코드funcGNN: A Graph Neural Network Approach to Program Similarity
https://github.com/aravindsankar28/DySAT
공식 코드Dynamic Graph Representation Learning via Self-Attention Networks
https://github.com/armitakhn/gilbert
공식 코드https://github.com/arthurcgusmao/XKE
공식 코드Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
https://github.com/aryopg/biokge
공식 코드https://github.com/asappresearch/kbc-pomr
공식 코드Knowledge Base Completion for Constructing Problem-Oriented Medical Records
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.