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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

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Showing 171–180 of 403 papers

TitleStatusHype
Equivalence between LINE and Matrix Factorization—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
Hierarchical Graph Neural Networks—0
Heterogeneous Edge Embeddings for Friend Recommendation—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
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