In the past decade, semi-supervised learning (SSL) over graphs has gained popularity due
to its importance in a gamut of network science applications. While most of existing SSL …

Artificial intelligence (AI), a well-known subject of computer science, has several therapeutic
applications, including the analysis of intricate medical data and the extraction of significant …

Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of
network science applications. However, most SSL approaches rely on deterministic …

Graph-guided semi-supervised learning (SSL) has gained popularity in several network
science applications, including biological, social, and financial ones. SSL becomes …

Graph-guided semi-supervised learning (SSL) and inference has emerged as an attractive
research field thanks to its documented impact in a gamut of application domains, including …

Given nodal observations that can be limited due to sampling costs or privacy concerns,
several network-science-related applications entail reconstruction of values on all network …

Graph-based inference arises in a gamut of network science-related applications, including
smart transportation, climate forecasting, and neuroscience. Given observations over a …

Inference of spatio-temporal processes over graphs arises in a gamut of network science-
related applications, including smart transportation, climate forecasting, and neuroscience …

Current spatiotemporal learning methods for complex data exploit the graph structure as an
inductive bias to restrict the function space and improve data and computation efficiency …

Abstract Machine learning (ML) has gained popularity due to its well-documented merits in
several inference tasks across diverse applications including healthcare, robotics and …