https://github.com/miralab-ustc/gcn4kgc
공식 코드Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Papers with Code 아카이브
Graph Embedding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/mjsumpter/remod
공식 코드https://github.com/mlelarge/graph_neural_net
공식 코드Expressive Power of Invariant and Equivariant Graph Neural Networks
https://github.com/moen-hyb/Graph2MDA
공식 코드Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations
https://github.com/morteza/cogtext
공식 코드https://github.com/mtanveer1/ge-ifrvfl-cil
공식 코드https://github.com/navid-naderi/WEGL
공식 코드https://github.com/nec-research/emu-kg
공식 코드Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
https://github.com/netpaladinx/DPMPN
공식 코드Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
https://github.com/netpaladinx/NeuCFlow
공식 코드https://github.com/ngl567/cake
공식 코드CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
https://github.com/nhuang37/spectral-inspired-gnn
공식 코드From Local to Global: Spectral-Inspired Graph Neural Networks
https://github.com/nicolas-hbt/benchmark-sematk
공식 코드Sem@$K$: Is my knowledge graph embedding model semantic-aware?
https://github.com/nicolas-hbt/semantic-lossfunc
공식 코드https://github.com/nicolas-hbt/similar-embeddings
공식 코드https://github.com/nicolas-hbt/versatile-embeddings
공식 코드Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
https://github.com/nik-v9/airobject
공식 코드AirObject: A Temporally Evolving Graph Embedding for Object Identification
https://github.com/niraj17singh/linklogic
공식 코드LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
https://github.com/niva-knowledge-graph/kgs_and_effect_prediction_2020
공식 코드Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
https://github.com/nju-websoft/HyperKA
공식 코드Knowledge Association with Hyperbolic Knowledge Graph Embeddings
https://github.com/nju-websoft/MultiKE
공식 코드https://github.com/nju-websoft/cole
공식 코드I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
https://github.com/nju-websoft/knowla
공식 코드KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
https://github.com/nju-websoft/mukg
공식 코드$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
https://github.com/nju-websoft/unify-ea-sf
공식 코드https://github.com/nk-ruiying/tcompounde
공식 코드Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
https://github.com/nle-ml/mmkb
공식 코드Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
https://github.com/nlp-rl/ki-ddi
공식 코드Towards Knowledge-Infused Automated Disease Diagnosis Assistant
https://github.com/noycohen100/MARCO-GE
공식 코드Automatic selection of clustering algorithms using supervised graph embedding
https://github.com/nslab-cuk/connector
공식 코드Connector 0.5: A unified framework for graph representation learning
https://github.com/nslab-cuk/literalkg
공식 코드https://github.com/nyushcs/uniglm
공식 코드UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
https://github.com/oliver-lloyd/kge_param_sens
공식 코드Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality
https://github.com/onixhoque/sptransx-mlsys2025-reproduce
공식 코드https://github.com/otamio/kga
공식 코드https://github.com/otouat/gnnevaluationmetrics
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.