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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 111–120 of 403 papers

TitleStatusHype
QUINT: Node embedding using network hashing—0
REFINE: Random RangE FInder for Network Embedding—0
Temporal Network Embedding via Tensor Factorization—0
Signed Bipartite Graph Neural NetworksCode1
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Deep Contrastive Multiview Network Embedding—0
TextCNN with Attention for Text ClassificationCode0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
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