Time-varying graph representation learning via higher-order skip-gram with negative sampling

Abstract Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these t...

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Bibliographic Details
Main Authors: Simone Piaggesi, André Panisson
Format: Article
Language:English
Published: SpringerOpen 2022-05-01
Series:EPJ Data Science
Subjects:
Online Access:https://doi.org/10.1140/epjds/s13688-022-00344-8