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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 371–380 of 403 papers

TitleStatusHype
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks—0
Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction—0
Large-scale Gender/Age Prediction of Tumblr Users—0
Large-Scale Network Embedding in Apache Spark—0
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks—0
Latent Network Embedding via Adversarial Auto-encoders—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
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