Learning embeddings for multiplex networks using triplet loss

Seyedsaeed Hajiseyedjavadi
Seyedsaeed Hajiseyedjavadi
Konstantinos Pelechrinis
Konstantinos Pelechrinis

Applied Network Science, pp. 1-16, 2019.

Cited by: 0|Bibtex|Views39|DOI:https://doi.org/10.1007/s41109-019-0242-0
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Other Links: dblp.uni-trier.de|link.springer.com

Abstract:

Learning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, community detection, etc. However, to date, the vast majority of these learning tasks are focused on traditional single-layer/unimodal...More

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