https://github.com/zjunet/damf
공식 코드Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Papers with Code 아카이브
Network Embedding
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Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2myeonggyu/Graph-Embedding
https://github.com/3480430977/ACDNE
Adversarial Deep Network Embedding for Cross-network Node Classification
https://github.com/Awadelrahman/GNN4SocialNWTutorial
https://github.com/BDonnot/leap_net
https://github.com/DeepGraphLearning/graphvite
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
https://github.com/FDUDSDE/Subg-Con
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
https://github.com/GeminiLight/virne
https://github.com/HomoSapienLCY/Contextual_Regression
https://github.com/Jerry2398/DANE-Simple-implementation
https://github.com/Jhy1993/HAN
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
https://github.com/Kaslanarian/SAGOD
https://github.com/KrishnaswamyLab/GeometryAwareGenerativeAutoencoder
https://github.com/Linear95/DetGP
Dynamic Embedding on Textual Networks via a Gaussian Process
https://github.com/MS-Mind/MS-Code-01/tree/main/textcnn
https://github.com/MindCode-4/code-8/tree/main/mvn2vec
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
https://github.com/MindSpore-scientific/code-6/tree/main/mvn2vec
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
https://github.com/RingBDStack/DP-CroSUE/tree/main/DP-CroSUE-mindspore/
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
https://github.com/RingBDStack/RWNE
https://github.com/SY1706203/GATNE
Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/THUDM/GATNE
Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/abhilash1910/Deep-Graph-Learning
https://github.com/abojchevski/graph2gauss
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
https://github.com/aida-ugent/NRLBenchmark
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
https://github.com/arangoml/fastgraphml
https://github.com/baichuan/disambiguation_embedding
Name Disambiguation in Anonymized Graphs using Network Embedding
https://github.com/benedekrozemberczki/BoostedFactorization
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
https://github.com/benedekrozemberczki/FSCNMF
https://github.com/benedekrozemberczki/karateclub
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
https://github.com/benedekrozemberczki/littleballoffur
https://github.com/benedekrozemberczki/role2vec
https://github.com/boom85423/hello_SHINE
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
https://github.com/chentingpc/GuidedHeteEmbedding
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
https://github.com/dmlc/dgl/tree/master/examples/pytorch/GATNE-T
Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat
Graph Representation Learning via Hard and Channel-Wise Attention Networks
https://github.com/dmlc/dgl/tree/master/examples/pytorch/rect
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.