科學合作網絡的綜合研究:基於網絡分析技術

Abstract

隨著研究主題變得專業化和多元化，政府和組織如何有效分配有限的研究資源已變得至關重要但極具挑戰性。馬太效應是指成功的作者往往會更加成功，而知名度較低的作者則往往難以獲得認可的現象，這種現象在科學合作網絡中被觀察到。在我們之前的研究中，我們在不同假設條件下建立了科學合作網絡模型，並量化了流行效應（也稱為馬太效應[15]）對研究人員和學科的影響，以及研究人員在該學科中的天才程度。在本文中，我們將此模型應用於從科學網（WoS）資料庫收集的不同學科，並根據模型中每個學科的參數對其進行分析。我們使用這些指標創建了一個圖表，並分析了任意兩個學科之間這兩個指標相似性的關係。

As research topics become specialized and diverse, it has become crucial but challenging for governments and organizations to distribute limited research resources effectively. The Matthew effect, which refers to a phenomenon where successful authors tend to be more successful while lesser-known authors tend to struggle to gain recognition, is observed in the scientific collaboration network. In our prior research, we developed a scientific collaboration network model under different hypotheses and quantified the impact of the popular effect (also known as the Matthew effect[15]) on both the researcher and the subject, as well as the researcher's level of genius within the subject. In this paper, we apply this model to different subjects collected from the Web of Science (WoS) database and analyze them based on the parameters of each subject in the model. We used these indicators to create a plot and analyze the relationship between the similarities of the two indicators for any two subjects.