  The development of science constitutes itself an important subject of
scientific investigation.Indeed, better knowledge about this intricate
dynamical system can provide subsidies for enhancing the manners in which
science progresses. Recently, a network science-based approach was reported
aimed at characterizing and studying the prospects for scientific advancement
assuming that new pieces of knowledge are incorporated in a uniformly random
manner. A surprising result was reported in the sense that quite similar
advancements were observe for both Erd\H{o}s-R\'enyi (ER) and Barab\'asi-Albert
(BA) knowledge networks. In the present work, we develop a systematic
complementation of that preliminary investigation, considering an additional
network model, the random geometric graph (GR) as well as several other manners
to incorporating knowledge, namely preferential to node degree, preferential to
closer or adjacent nodes, as well as taking into account the betweenness
centrality of the unknown nodes. Several interesting results were obtained and
discussed, including: the uniform strategy led to the best expansion in the GR
model, the results of the degree and betweenness expansion for the ER and GR
models were similar to those obtained using uniformity method in those same
models. Surprisingly, the BA model led to just a slightly faster expansion than
in the uniform case. Though qualitatively similar, strategies based on the
degree and betweenness yielded distinct results as a consequence of them not
being linearly related.
