Graph Embedding
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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/google-research/google-research/tree/master/graph_embedding/persona
공식 코드Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
https://github.com/google-research/google-research/tree/master/private_personalized_pagerank
공식 코드Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
https://github.com/google/gcnn-survey-paper
공식 코드Machine Learning on Graphs: A Model and Comprehensive Taxonomy
https://github.com/gracexu182/dyng2g
공식 코드DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
https://github.com/graphcore-research/kg-topology-toolbox
공식 코드The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
https://github.com/graphcore/distributed-kge-poplar
공식 코드BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
https://github.com/guilhermesfc/ontology-matching-absolute-orientation
공식 코드Ontology Matching Through Absolute Orientation of Embedding Spaces
https://github.com/guojiapub/bique
공식 코드https://github.com/hessamla/forcedirected
공식 코드https://github.com/heyuntian/fairmile
공식 코드FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
https://github.com/hipgraph/sptransx
공식 코드https://github.com/hkust-knowcomp/complexhyperbolickge
공식 코드Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
https://github.com/hkust-knowcomp/privategraphencoder
공식 코드Independent Distribution Regularization for Private Graph Embedding
https://github.com/hl4220/physics-informed-neural-network-with-graph-embedding
공식 코드https://github.com/hmthanh/GCAT
공식 코드Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
https://github.com/honggen-zhang/hasa-ckge
공식 코드HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
https://github.com/hopkin-ghp/QuatDE
공식 코드QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
https://github.com/huda-hakami/learning-to-borrow-for-kgs
공식 코드https://github.com/hugochan/IDGL
공식 코드Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
https://github.com/hwwang55/MKR
공식 코드Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
https://github.com/hysonlab/biomedkg
공식 코드Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
https://github.com/hyunwookl/testam
공식 코드TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
https://github.com/hyzhang98/AdaGAE
공식 코드https://github.com/i40-Tools/I40KG-Embeddings
공식 코드Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
https://github.com/i721/GraphCoordinates
공식 코드Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks
https://github.com/ibalazevic/HypER
공식 코드https://github.com/iieir-km/ComplEx-NNE_AER
공식 코드Improving Knowledge Graph Embedding Using Simple Constraints
https://github.com/iieir-km/RUGE
공식 코드Knowledge Graph Embedding with Iterative Guidance from Soft Rules
https://github.com/informagi/GEEER
공식 코드https://github.com/innvariant/deepgg
공식 코드https://github.com/ins1stenc3/gder
공식 코드GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
https://github.com/intfloat/simkgc
공식 코드SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
https://github.com/iodike/chronokge
공식 코드Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations
https://github.com/ireklos/pathe
공식 코드PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
https://github.com/jackmiemie/GMEL
공식 코드Learning Geo-Contextual Embeddings for Commuting Flow Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.