Modeling the Perspectives for Scientific Advancement

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 Erdős–Rényi (ER) and Barabási–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.