In social network analysis, the detection of communities—composed of people with common interests—is a classical problem. Moreover, people can somehow influence any other in the community, i.e., they can spread information among them. In this paper, two models are proposed considering information diffusion strategies and the identification of communities in a scientific social network built through these two model concepts. The maximum flow-based and the Erdös number-based models are proposed as a measurement to weigh all the relationships between elements. A clustering algorithm (k-medoids) was used for the identification of communities of closely connected people in order to evaluate the proposed models in a scientific social network. Detailed analysis of the obtained scientific communities was conducted to compare the structure of formed groups and to demonstrate the feasibility of the solution. The results demonstrate the viability and effectiveness of the proposed solution, showing that information reaches elements that are not directly related to the element that produces it.

中文翻译：

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有理 Erdös 数和最大流量作为科学社会网络分析的测量模型

在社交网络分析中，由具有共同兴趣的人组成的社区的检测是一个经典问题。此外，人们可以以某种方式影响社区中的任何其他人，即他们可以在他们之间传播信息。在本文中，考虑到信息传播策略和通过这两个模型概念构建的科学社会网络中的社区识别，提出了两个模型。提出了基于最大流量和基于 Erdös 数的模型作为衡量元素之间所有关系的度量。聚类算法 (k-medoids) 用于识别密切联系的人的社区，以便在科学社交网络中评估所提出的模型。对获得的科学共同体进行了详细分析，以比较形成的群体的结构并证明解决方案的可行性。结果证明了所提议解决方案的可行性和有效性，表明信息可以到达与生成它的元素没有直接关系的元素。