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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
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Network Embedding Using Sparse Approximations of Random Walks
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Collaborative Graph Neural Networks for Attributed Network Embedding
Source-Aware Embedding Training on Heterogeneous Information Networks
Random Walk on Multiple Networks
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Graph-Level Embedding for Time-Evolving Graphs
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
Semantic Random Walk for Graph Representation Learning in Attributed Graphs
Zoo Guide to Network Embedding
$\text{H}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
Space-Invariant Projection in Streaming Network Embedding
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection
Cross Version Defect Prediction with Class Dependency Embeddings
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives
Recommending on graphs: a comprehensive review from a data perspective
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Associative Learning for Network Embedding
Multiplex Heterogeneous Graph Convolutional Network
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
NECA: Network-Embedded Deep Representation Learning for Categorical Data
Unsupervised Network Embedding Beyond Homophily
Deep Partial Multiplex Network Embedding
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding
Grammar-Based Grounded Lexicon Learning
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