For SVM and TSVM, the increase of labeled instances means more labeled data are available, so the classifiers perform better.
for label propagation when more seeds take part in the label propagating process, unlabeled vertices could get more reliable sources so that LP could become more confident to decide the label one specific vertex belongs to.