https://github.com/memexproject/memex-kg
공식 코드Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Papers with Code 아카이브
Graph Representation Learning
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The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mengyingjiang/hmgrl
공식 코드https://github.com/merialdo/research.lpca
공식 코드Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
https://github.com/mhadnanali/febaa
공식 코드Features Based Adaptive Augmentation for Graph Contrastive Learning
https://github.com/michiyasunaga/qagnn
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/mikhail247/iamax
공식 코드https://github.com/miralab-ustc/gcn4kgc
공식 코드Rethinking Graph Convolutional Networks in Knowledge Graph Completion
https://github.com/mishne-lab/ignr
공식 코드https://github.com/mocherson/Exp_GNN
공식 코드https://github.com/namkyeong/rgrl
공식 코드https://github.com/nanw16/GTNet
공식 코드https://github.com/nashchou/multigprompt
공식 코드MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
https://github.com/navid-naderi/LinkSchedulingGNNs_SupervisionStudy
공식 코드https://github.com/nd7141/bgnn
공식 코드Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
https://github.com/nec-research/emu-kg
공식 코드Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
https://github.com/nec-research/graph-sum-product-networks
공식 코드Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
https://github.com/nerdslab/bgrl
공식 코드Large-Scale Representation Learning on Graphs via Bootstrapping
https://github.com/neutralpronoun/general-gcl
공식 코드Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
https://github.com/ngl567/cake
공식 코드CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
https://github.com/nicknakis/hbdm
공식 코드A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
https://github.com/nicknakis/hm-ldm
공식 코드A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
https://github.com/nicknakis/slim_raa
공식 코드Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
https://github.com/nickskyyy/lcc-for-gc
공식 코드An Efficient Loop and Clique Coarsening Algorithm for Graph Classification
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/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/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/mukg
공식 코드$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
https://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/nslab-cuk/connector
공식 코드Connector 0.5: A unified framework for graph representation learning
https://github.com/nslab-cuk/literalkg
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.