Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
PLoS One. 2021 Mar 18;16(3):e0248724. doi: 10.1371/journal.pone.0248724. eCollection 2021.
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.
科学知识不能被视为一组孤立的领域，而应被视为一个高度互联的网络。了解研究领域之间的联系对于合理分配资金和人力资源至关重要（例如，组建团队来解决多学科问题）。学科之间的关系可以从单个科学家的研究轨迹数据中得出，因为研究人员通常在一小部分相互关联的领域做出贡献。最近的两项研究工作提出了从科学家的出版物记录中创建研究图谱的方法：一种是使用频率派方法创建转移概率矩阵；另一种是通过学习嵌入（向量表示）。令人惊讶的是，这些模型是在不同的数据集上进行评估的，在文献中从未进行过比较。在这项工作中，我们使用来自巴西研究人员的大型出版物记录数据集，以系统的方式比较了这两种模型。我们评估了这些模型预测给定实体（科学家、机构或地区）相对于 ROC 曲线下面积进入新领域的能力。此外，我们分析了每种方法对出版物数量和与一个实体相关联的领域数量的敏感性。最后，我们进行了一个案例研究，展示了如何在巴西的背景下使用这些模型来描述科学动态。
