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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0
VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks
A multi-domain virtual network embedding algorithm with delay prediction
Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
Network Resource Allocation Strategy Based on Deep Reinforcement Learning
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks
Learning Large-scale Network Embedding from Representative Subgraph
WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows
Network representation learning: A macro and micro view
MetaMIML: Meta Multi-Instance Multi-Label Learning
High-order joint embedding for multi-level link prediction
Community detection using low-dimensional network embedding algorithms
Barlow Graph Auto-Encoder for Unsupervised Network Embedding
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation
Vaccine skepticism detection by network embedding
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling
Multi-Relation Aware Temporal Interaction Network Embedding
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
Latent Network Embedding via Adversarial Auto-encoders
Network representation learning systematic review: ancestors and current development state
QUINT: Node embedding using network hashing
REFINE: Random RangE FInder for Network Embedding
Signed Bipartite Graph Neural Networks
Temporal Network Embedding via Tensor Factorization
Semi-supervised Network Embedding with Differentiable Deep Quantisation
SiReN: Sign-Aware Recommendation Using Graph Neural Networks
Temporal Graph Network Embedding with Causal Anonymous Walks Representations
Deep Contrastive Multiview Network Embedding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.