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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 1120 of 403 papers

TitleStatusHype
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
A Survey on Role-Oriented Network EmbeddingCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
DANE: Domain Adaptive Network EmbeddingCode1
Adversarial Training Methods for Network EmbeddingCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
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