Semi-supervised and unsupervised machine learning methods often rely on graphs to model data, prompting research how theoretical properties of operators are leveraged in problems. While most the existing literature focuses undirected graphs, directed very important practice, giving models for physical, biological or transportation networks, among many other applications. In this paper, we propo...

Abstract Tackling semi-supervised learning problems with graph-based methods has become a trend in recent years since graphs can represent all kinds of data and provide suitable framework for studying continuum limits, example, differential operators. A popular strategy here is p -Laplacian learning, which poses smoothness condition on the sought inference function set unlabeled data. For $$p&l...