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 271–280 of 403 papers

TitleStatusHype
Initialization for Network Embedding: A Graph Partition Approach—0
NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
LEAP nets for power grid perturbationsCode0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Domain-adversarial Network AlignmentCode0
HONEM: Learning Embedding for Higher Order Networks—0
Deep Hashing for Signed Social Network Embedding—0
ProNE: Fast and Scalable Network Representation LearningCode0
Show:102550
← PrevPage 28 of 41Next →

No leaderboard results yet.