SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 131–140 of 403 papers

TitleStatusHype
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
Robust Dynamic Network Embedding via EnsemblesCode1
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender SystemsCode0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Hierarchical Graph Neural Networks—0
Show:102550
← PrevPage 14 of 41Next →

No leaderboard results yet.