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 techniques can be defined both for static and for time-varying graphs. Here, we show how the skip-gram embedding approach can be generalized to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face p

SUBMITTER: Piaggesi S 

PROVIDER: S-EPMC9143726 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature