  Research accomplishment is usually measured by considering all citations with
equal importance, thus ignoring the wide variety of purposes an article is
being cited for. Here, we posit that measuring the intensity of a reference is
crucial not only to perceive better understanding of research endeavor, but
also to improve the quality of citation-based applications. To this end, we
collect a rich annotated dataset with references labeled by the intensity, and
propose a novel graph-based semi-supervised model, GraLap to label the
intensity of references. Experiments with AAN datasets show a significant
improvement compared to the baselines to achieve the true labels of the
references (46% better correlation). Finally, we provide four applications to
demonstrate how the knowledge of reference intensity leads to design better
real-world applications.
