2 papers
cs.LG2020
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
Simone Piaggesi, André 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. S…
physics.soc-ph2019
Maximum entropy approaches for the study of triadic motifs in the Mergers & Acquisitions network
Ihusan Adam, Stefano Garlaschi, Jian-Hong Lin +4
In the past years statistical physics has been successfully applied for complex networks modelling. In particular, it has been shown that the maximum entropy principle can be explo…