Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study

Abstract

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.

Analysis of co-authorship networks among Brazilian graduate programs in computer science

“…A significant number of analyses on academic collaborations between researchers have been made since the establishment of the field of “Scientometrics” in the 70s, although works with similar ideas date as further back as the early 20th century [ 10 ]. Some examples of analyses of academic collaborations include nation-wide investigations, such as in Slovenia [ 11 ], Brazil [ 12 – 16 ], Germany [ 17 ] and Turkey [ 18 ]. Other works restrict the analysis to a certain discipline within a country, for example, conducting an analysis of only computer science publications in Brazil [ 19 ].…”

On network backbone extraction for modeling online collective behavior

“…To generalize and tackle our target problem, in the following we propose a methodology to target the studies of collective behavior emerging from networks. Despite the focus on social media, our proposed methodology is generic enough to be applied to phenomena in other online and offline domains that are also modeled by noisy networks (e.g., co-voting [ 69 , 70 ] and co-authorship [ 65 ] networks).…”

“…Starting with applications, multiple works in various fields have shown the importance of backbone extraction methods to deal with random, sporadic, and weak edges that may obfuscate the phenomenon under study. For example, several studies applied early proposed methods to study phenomena in biological networks [ 59 , 60 ], economic networks [ 61 – 63 ], co-authoring networks [ 64 , 65 ], human mobility networks [ 66 – 68 ] as well as congressional voting networks [ 25 , 69 , 70 ].…”