https://github.com/lijingzhu1/ett-ckge
공식 코드ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
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/ling-cai/time2box
공식 코드Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
https://github.com/lion-zs/gie
공식 코드https://github.com/liseda-lab/seek
공식 코드Explainable Representations for Relation Prediction in Knowledge Graphs
https://github.com/liseda-lab/truewalks
공식 코드Biomedical Knowledge Graph Embeddings with Negative Statements
https://github.com/liyouru0228/hgv
공식 코드HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
https://github.com/llqy123/dabr
공식 코드Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
https://github.com/lukashedegaard/dage
공식 코드https://github.com/luoxiaoheics/stancerel
공식 코드https://github.com/mahsa91/py_mgcn
공식 코드MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
https://github.com/malllabiisc/CompGCN
공식 코드Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/malllabiisc/ConfGCN
공식 코드Neural Graph Embedding Methods for Natural Language Processing
https://github.com/malllabiisc/InteractE
공식 코드InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
https://github.com/malteos/pytorch-bert-document-classification
공식 코드Enriching BERT with Knowledge Graph Embeddings for Document Classification
https://github.com/mandt-lab/knowledge-graph-tuning
공식 코드https://github.com/marcob46/mpxgat
공식 코드MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
https://github.com/marina-sp/classification_lp
공식 코드Ranking vs. Classifying: Measuring Knowledge Base Completion Quality
https://github.com/marius-team/marius
공식 코드Marius: Learning Massive Graph Embeddings on a Single Machine
https://github.com/marlin-codes/FeatureNorm
공식 코드FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
https://github.com/marlin-codes/HTGN
공식 코드Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
https://github.com/masashi-y/abduction_kbc
공식 코드Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
https://github.com/mashaan14/ASC-noisy
공식 코드Approximate spectral clustering density-based similarity for noisy datasets
https://github.com/mashaan14/ASC-self-tuned-k
공식 코드Approximate spectral clustering with eigenvector selection and self-tuned $k$
https://github.com/mashaan14/PAtree-SGC
공식 코드Graph Construction using Principal Axis Trees for Simple Graph Convolution
https://github.com/mashaan14/RPTree-GCN
공식 코드Random Projection Forest Initialization for Graph Convolutional Networks
https://github.com/mashaan14/RPTree-SpectralNet
공식 코드https://github.com/mashaan14/SC-parameter-free
공식 코드A parameter-free graph reduction for spectral clustering and SpectralNet
https://github.com/mashaan14/Spectral-Clustering
공식 코드Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
https://github.com/matthiasrlalisse/hmemnetworks
공식 코드Scalable knowledge base completion with superposition memories
https://github.com/maxuewei2/RiWalk
공식 코드RiWalk: Fast Structural Node Embedding via Role Identification
https://github.com/mdimiccoli/hm-sge
공식 코드Learning grounded word meaning representations on similarity graphs
https://github.com/memexproject/memex-kg
공식 코드Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
https://github.com/mengyuan-lee/graph_embedding_link_scheduling
공식 코드Graph Embedding based Wireless Link Scheduling with Few Training Samples
https://github.com/merialdo/research.lpca
공식 코드Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
https://github.com/mianzg/kgbiasdetec
공식 코드https://github.com/mickey253/l2g
공식 코드Balancing between the Local and Global Structures (LGS) in Graph Embedding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.