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

Showing 151160 of 403 papers

TitleStatusHype
Scalable Hierarchical Embeddings of Complex Networks0
Network representation learning systematic review: ancestors and current development state0
QUINT: Node embedding using network hashing0
REFINE: Random RangE FInder for Network Embedding0
Temporal Network Embedding via Tensor Factorization0
Semi-supervised Network Embedding with Differentiable Deep Quantisation0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Deep Contrastive Multiview Network Embedding0
TextCNN with Attention for Text ClassificationCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks0
Show:102550
← PrevPage 16 of 41Next →

No leaderboard results yet.