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/astrazeneca/biomedical-kg-topological-imbalance
공식 코드Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
https://github.com/astrazeneca/kgem-in-drug-discovery
공식 코드Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/basiralab/AGSR-Net
공식 코드https://github.com/basiralab/HCAE
공식 코드Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification
https://github.com/basiralab/HUNet
공식 코드Deep Hypergraph U-Net for Brain Graph Embedding and Classification
https://github.com/bdi-lab/bive
공식 코드Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
https://github.com/bdi-lab/hynt
공식 코드Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
https://github.com/bdi-lab/ingram
공식 코드InGram: Inductive Knowledge Graph Embedding via Relation Graphs
https://github.com/benedekrozemberczki/datasets
공식 코드Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
https://github.com/benedekrozemberczki/diff2vec
공식 코드https://github.com/benedekrozemberczki/karateclub
공식 코드Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
https://github.com/benedekrozemberczki/littleballoffur
공식 코드https://github.com/bhagya-hettige/GLACE
공식 코드https://github.com/bhushank/kge-rl
공식 코드Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
https://github.com/bio-ontology-research-group/DELE
공식 코드DELE: Deductive $\mathcal{EL}^{++} \thinspace $ Embeddings for Knowledge Base Completion
https://github.com/bio-ontology-research-group/cate
공식 코드Lattice-preserving $\mathcal{ALC}$ ontology embeddings with saturation
https://github.com/bio-ontology-research-group/falcon
공식 코드https://github.com/bio-ontology-research-group/ontology-graph-projections
공식 코드https://github.com/boniolp/kGraph
공식 코드$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
https://github.com/borealisai/staypositive
공식 코드Stay Positive: Knowledge Graph Embedding Without Negative Sampling
https://github.com/boschresearch/llm-vs-gibberish-ontologies
공식 코드Do LLMs Really Adapt to Domains? An Ontology Learning Perspective
https://github.com/box-el/boxel
공식 코드https://github.com/brickee/zerokbc
공식 코드ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
https://github.com/bveseli/lmsforkbc
공식 코드https://github.com/bzho3923/magnet
공식 코드Robust Graph Representation Learning for Local Corruption Recovery
https://github.com/cai-lw/KBGAN
공식 코드https://github.com/caojiangxia/BiGI
공식 코드Bipartite Graph Embedding via Mutual Information Maximization
https://github.com/carolinlawrence/gradient-rollback
공식 코드Explaining Neural Matrix Factorization with Gradient Rollback
https://github.com/catarina-canastra/benchmarking-knowledge-graph-embeddings-for-gene-disease-association-prediction
공식 코드A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction
https://github.com/causalkg/causallp
공식 코드CausalLP: Learning causal relations with weighted knowledge graph link prediction
https://github.com/cenyk1230/GATNE
공식 코드Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/changyi7231/nfe
공식 코드https://github.com/charlie-xiao/embedding-visualization-test
공식 코드https://github.com/charusharma1991/LinkPred
공식 코드Learning Representations using Spectral-Biased Random Walks on Graphs
https://github.com/chenwei-zhang/ViDa
공식 코드Visualizing DNA reaction trajectories with deep graph embedding approaches
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.