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Network Embedding

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

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 81–90 of 403 papers

TitleStatusHype
VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making—0
Network Resource Allocation Strategy Based on Deep Reinforcement Learning—0
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode—0
A multi-domain virtual network embedding algorithm with delay prediction—0
Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method—0
Collaborative filtering via heterogeneous neural networks—0
PPPNE: Personalized proximity preserved network embedding—0
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