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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 3140 of 403 papers

TitleStatusHype
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
DANE: Domain Adaptive Network EmbeddingCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Adversarial Training Methods for Network EmbeddingCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
Outlier Aware Network Embedding for Attributed NetworksCode1
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