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 4150 of 403 papers

TitleStatusHype
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
A Survey on Role-Oriented Network EmbeddingCode1
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Multiplex Heterogeneous Graph Convolutional NetworkCode1
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
Show:102550
← PrevPage 5 of 41Next →

No leaderboard results yet.