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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
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Subset-Contrastive Multi-Omics Network Embedding
Network Alignment
Network Embedding Exploration Tool (NEExT)
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Stationary distribution of node2vec random walks on household models
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism
A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Non-Euclidean Mixture Model for Social Network Embedding
Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Simultaneous Weight and Architecture Optimization for Neural Networks
Simplifying complex machine learning by linearly separable network embedding spaces
Efficient Network Embedding by Approximate Equitable Partitions
A Survey on Signed Graph Embedding: Methods and Applications
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
Simplicity within biological complexity
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
Detecting local perturbations of networks in a latent hyperbolic embedding space
Clustering Molecular Energy Landscapes by Adaptive Network Embedding
BHGNN-RT: Network embedding for directed heterogeneous graphs
Hedging carbon risk with a network approach
A Simple and Powerful Framework for Stable Dynamic Network Embedding
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
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