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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/cspjiao/RONE
공식 코드https://github.com/daokunzhang/attri2vec
공식 코드https://github.com/daokunzhang/binaryne
공식 코드https://github.com/ds4ai-upb/danes
공식 코드https://github.com/edwarddavis1/universal_dynamic_embedding_with_testing
공식 코드A Simple and Powerful Framework for Stable Dynamic Network Embedding
https://github.com/elmspace/neext
공식 코드https://github.com/ericschulman/fonts_causal_analysis
공식 코드Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts
https://github.com/ferencberes/covid-vaccine-network
공식 코드https://github.com/flyingdoog/rwm
공식 코드https://github.com/geminilight/hrl-acra
공식 코드https://github.com/google/gcnn-survey-paper
공식 코드Machine Learning on Graphs: A Model and Comprehensive Taxonomy
https://github.com/houchengbin/ABRW
공식 코드Attributed Network Embedding for Incomplete Attributed Networks
https://github.com/houchengbin/DynWalks
공식 코드DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
https://github.com/houchengbin/GloDyNE
공식 코드GloDyNE: Global Topology Preserving Dynamic Network Embedding
https://github.com/houchengbin/SG-EDNE
공식 코드https://github.com/hse-dyngraph-research-team/dyngraphmodelling
공식 코드Temporal Graph Network Embedding with Causal Anonymous Walks Representations
https://github.com/huangjunjie-cs/SBGNN
공식 코드https://github.com/huangjunjie95/SiGAT
공식 코드https://github.com/hyzhang98/AdaGAE
공식 코드https://github.com/jordan7186/Edgeless-GNN-external
공식 코드Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
https://github.com/khushnood/TempNodeEmbed_upload
공식 코드TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix
https://github.com/kmj0792/trustsgcn
공식 코드Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
https://github.com/lemmonation/APNE
공식 코드Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
https://github.com/lemmonation/G2-EMF
공식 코드Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
https://github.com/leoguti85/GraphEmbs
공식 코드Unsupervised Network Embedding for Graph Visualization, Clustering and Classification
https://github.com/leoribeiro/struc2vec
공식 코드struc2vec: Learning Node Representations from Structural Identity
https://github.com/llan-ml/MetaTNE
공식 코드Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
https://github.com/mahsa91/py_mgcn
공식 코드MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
https://github.com/marfiori/efficient-ase
공식 코드Gradient-Based Spectral Embeddings of Random Dot Product Graphs
https://github.com/marlin-codes/HTGN
공식 코드Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
https://github.com/maxuewei2/RiWalk
공식 코드RiWalk: Fast Structural Node Embedding via Role Identification
https://github.com/melkael/vne
공식 코드Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding
https://github.com/naihemeng/hawe
공식 코드Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
https://github.com/nicknakis/hm-ldm
공식 코드A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
https://github.com/nsssjss/mhgcn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.