  Scientific knowledge cannot be seen as a set of isolated fields, but as a
highly connected network. Understanding how research areas are connected is of
paramount importance for adequately allocating funding and human resources
(e.g., assembling teams to tackle multidisciplinary problems). The relationship
between disciplines can be drawn from data on the trajectory of individual
scientists, as researchers often make contributions in a small set of
interrelated areas. Two recent works propose methods for creating research maps
from scientists' publication records: by using a frequentist approach to create
a transition probability matrix; and by learning embeddings (vector
representations). Surprisingly, these models were evaluated on different
datasets and have never been compared in the literature. In this work, we
compare both models in a systematic way, using a large dataset of publication
records from Brazilian researchers. We evaluate these models' ability to
predict whether a given entity (scientist, institution or region) will enter a
new field w.r.t. the area under the ROC curve. Moreover, we analyze how
sensitive each method is to the number of publications and the number of fields
associated to one entity. Last, we conduct a case study to showcase how these
models can be used to characterize science dynamics in the context of Brazil.
