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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 201–210 of 403 papers

TitleStatusHype
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
SepNE: Bringing Separability to Network Embedding—0
Signed Graph Diffusion Network—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
Distributed Representations of Signed Networks—0
Simplicity within biological complexity—0
Simplifying complex machine learning by linearly separable network embedding spaces—0
Source-Aware Embedding Training on Heterogeneous Information Networks—0
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