Methodology to identify demand-side low-carbon innovations and their potential impact on socio-technical energy systems

The rapid diffusion of demand-side low-carbon innovations has been identified as a key strategy for maintaining average global temperature rise at or below 1.5 °C. Diffusion research tends to focus on a single sector, or single technology case study, and on a small scope of factors that influence innovation diffusion. This paper describes a...

Active Semi-Supervised Learning for Diffusions on Graphs

Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...

Active Semi-Supervised Learning For Diffusions on Graphs

In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal...