Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling

25 Jun 2020Simone PiaggesiAndré Panisson

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 techniques can be defined both for static and for time-varying graphs... (read more)

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