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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 241–250 of 403 papers

TitleStatusHype
Temporal Network Embedding via Tensor Factorization—0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
Toward Edge-Centric Network Embeddings—0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks—0
Tutorial on NLP-Inspired Network Embedding—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
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