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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/pcy1302/TapEM
공식 코드https://github.com/pcy1302/asp2vec
공식 코드https://github.com/pige99/TME
공식 코드TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation
https://github.com/qiaoyut/conn
공식 코드Collaborative Graph Neural Networks for Attributed Network Embedding
https://github.com/roshnigiyer/nmm
공식 코드https://github.com/sambaranban/ONE
공식 코드https://github.com/samihaija/tf-fsvd
공식 코드https://github.com/sarathismg/gsne
공식 코드Boosting House Price Predictions using Geo-Spatial Network Embedding
https://github.com/shaido987/alarm-rca
공식 코드An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
https://github.com/shenxiaocam/ACDNE
공식 코드Adversarial Deep Network Embedding for Cross-network Node Classification
https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks
공식 코드Deep Network Embedding for Graph Representation Learning in Signed Networks
https://github.com/skx300/DyHATR
공식 코드https://github.com/srsohn/subtask-graph-execution
공식 코드Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
https://github.com/suchanuchp/BrainTWT
공식 코드https://github.com/szzhang17/Sparsest-Cut-Network-Embedding
공식 코드https://github.com/tailvyuanliang/h2tne
공식 코드$\text{H}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
https://github.com/tangjianpku/LINE
공식 코드https://github.com/tonyauyeung/local2gae2global
공식 코드L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding
공식 코드Adversarial Privacy Preserving Graph Embedding against Inference Attack
https://github.com/uhh-lt/amharicmodels
공식 코드https://github.com/ujeong1/bluetempnet-ieee-data-2024
공식 코드BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social
https://github.com/vasco95/DMGD
공식 코드Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description
https://github.com/virresh/viking
공식 코드Adversarial Attack on Network Embeddings via Supervised Network Poisoning
https://github.com/winycg/l-mcl
공식 코드Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
https://github.com/winycg/mcl
공식 코드Mutual Contrastive Learning for Visual Representation Learning
https://github.com/woni-seo/siren-reco
공식 코드SiReN: Sign-Aware Recommendation Using Graph Neural Networks
https://github.com/wonniu/AdvT4NE_WWW2019
공식 코드https://github.com/xptree/NetMF
공식 코드Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
https://github.com/xptree/NetSMF
공식 코드NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
https://github.com/yangji9181/HNE
공식 코드Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
https://github.com/yuzhimanhua/HiGitClass
공식 코드HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
https://github.com/yzjiao/Subg-Con
공식 코드Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
https://github.com/zhiqiangzhongddu/selene
공식 코드https://github.com/zhoushengisnoob/HAHE
공식 코드HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
https://github.com/zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization
공식 코드Simultaneous Weight and Architecture Optimization for Neural Networks
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