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 71–80 of 403 papers

TitleStatusHype
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Simplicity within biological complexity—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding—0
Show:102550
← PrevPage 8 of 41Next →

No leaderboard results yet.