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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 181190 of 403 papers

TitleStatusHype
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
DINE: A Framework for Deep Incomplete Network Embedding0
Big Networks: A Survey0
Random Walks: A Review of Algorithms and Applications0
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning0
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