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
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Grammar-Based Grounded Lexicon Learning—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
A General Framework for Content-enhanced Network Representation Learning—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Compositional Network Embedding—0
ASBERT: Siamese and Triplet network embedding for open question answering—0
Show:102550
← PrevPage 14 of 41Next →

No leaderboard results yet.