Thanks to the widespread availability of large-scale datasets on scholarly outputs, science itself has come under the microscope with the aim of capturing a quantitative understanding of its workings. In this study, we leverage well-established cognitive models coming from anthropology in order to characterise the personal network of collaborations between scientists, i.e., the network considered from the standpoint of each individual researcher (referred to as ego network), in terms of the cognitive investment they devote to the different collaborations. Building upon these models, we study the interplay between the structure of academic collaborations, academic performance, and academic mobility at different career stages. We take into account both purely academic mobility (i.e., the number of affiliation changes) and geographical mobility (i.e., physical relocations to different countries). For our investigation, we rely on a dataset comprising the geo-referenced publications of a group of 81,500 authors extracted from Scopus, one of the biggest repositories of academic knowledge. Our main finding is that there is a clear correlation between the structure of co-authorship ego networks and academic performance indices: the more one publishes and the higher their impact, the larger their collaboration network. However, we observe a capacity bound effect, whereby, beyond a certain point, higher performances become increasingly less correlated with large collaboration networks. We also find that international academic migrants are better at growing their networks than researchers that only migrate within the same country, but the latter seem to be better in exploiting their collaboration to achieve higher impact. High academic mobility does not appear to translate into better academic performance or larger collaboration networks. This shows a different finding with respect to related literature, where scientific productivity is seen as directly linked to mobility. Our results show that, when looking at impact of research, this is not necessarily the case.

由于学术产出中大规模数据集的广泛可用性，科学本身已被纳入显微镜下，旨在获得对其工作的定量理解。在这项研究中，我们利用人类学建立的公认的认知模型来表征科学家之间的个人协作网络，即从每个研究者的角度考虑的网络（称为自我网络），他们致力于不同合作的认知投资。在这些模型的基础上，我们研究了不同职业阶段的学术合作结构，学术表现和学术流动性之间的相互作用。我们同时考虑了纯粹的学术流动性（即从属关系变更的次数）和地域流动性（即 搬迁到其他国家/地区）。对于我们的调查，我们依赖于一个数据集，该数据集包括从Scopus（最大的学术知识资料库之一）中摘录的一组81,500名作者的地理参考出版物。我们的主要发现是，共同作者自我网络的结构与学术绩效指数之间存在明显的相关性：发表次数越多，影响力越高，合作网络就越大。但是，我们观察到了容量限制效应，即在一定程度上，更高的性能与大型协作网络的相关性越来越小。我们还发现，国际学术移民比仅在同一国家/地区移民的研究人员更擅长发展网络，但是后者似乎在利用他们的协作以取得更大影响方面更好。高学术流动性似乎并未转化为更好的学术表现或更大的协作网络。这显示了与相关文献不同的发现，在相关文献中，科学生产力被视为与流动性直接相关。我们的结果表明，影响研究，不一定是这种情况。