Before revision:  Our findings show that all measures and methods vary strongly in accuracybetween data sets and that there is no clear best combination to employ in all cases  .
After revision:  Our findings show that all measures and methods vary strongly in accuracybetween data sets and that there is no clear best combination to employ in all cases is found. Our study provides a reproducible framework for future work in the direction of semi-supervised learning for time series with a focus on graph representations .