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 2130 of 403 papers

TitleStatusHype
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
Heterogeneous Network Representation Learning: A Unified Framework with Survey and BenchmarkCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Show:102550
← PrevPage 3 of 41Next →

No leaderboard results yet.