U.S.- Hungary Research on Social Networks as Evolving Complex Networks

卡拉马祖学院的Jan Tobochnik和布达佩斯Eotvos大学的George Kampis开展了这项由美国和匈牙利联合开展的研究项目，目的是研究社交网络的结构和动态。他们将从一个简单的社交网络形成和增长模型开始，该模型包括具有影响它们与其他节点连接方式的属性或特征的单个节点。反过来，研究人员将把他们的抽象模型与通过对学生进行调查创建的友谊网络的经验数据进行比较。将比较模型和调查结果，以确定形成连接的概率是否与两个节点之间的相似性函数成正比。通过观察某些物理属性，如作为控制参数的函数从多个集群到单个集群的相变，结果应改进对新兴网络属性的定义，特别是集群和网络的等级结构。移除随机节点的影响和一些基于博弈的效率措施观察也将被考虑在内。总体而言，这种新的跨学科方法应该会通过美国和匈牙利在系统建模、认知机制和计算数学方面的专家贡献来加强当前在网络方面的社会科学工作。预计结果将改进目前检查人、组织和社区等真实实体之间关系的方法。这项关于进化复杂网络的研究通过使美国和中欧的专家能够结合互补的人才并在相互感兴趣和能力很强的领域共享研究资源来实现促进科学知识的计划目标。更广泛的影响还包括通过在匈牙利机构的工作和直接参与该项目的中央网络调查，向美国学生介绍跨学科科学和国际研究社区。

The goal of this U.S.-Hungarian research project between Jan Tobochnik of Kalamazoo College and George Kampis of Eotvos University, in Budapest, is to study the structure and dynamics of social networks. They will begin with a simple model for the formation and growth of a social network that includes individual nodes with properties or traits that influence how they connect with other nodes. In turn, the researchers will compare their abstract model with empirical data from friendship networks created through surveys of students. The model and survey results will be compared to determine if the probability of forming a connection is proportional to a function of similarity between two nodes. Results should improve the definition of properties of emerging networks, especially the clustering and the hierarchical structure of networks, through observations of certain physical properties such as phase transition from many clusters to a single cluster as a function of control parameters. The effects of removing random nodes and some game-based observations of efficiency measures will be considered as well. Overall, the novel interdisciplinary approach should strengthen current social science work on networks through US-Hungarian expert contributions in systems modeling, mechanisms of cognition, and computational mathematics. Results are expected to improve present methods for examining relationships between real entities such as people, organizations and communities. This research on evolving complex networks fulfills the program objective of advancing scientific knowledge by enabling experts in the United States and Central Europe to combine complementary talents and share research resources in areas of strong mutual interest and competence. Broader impacts also include the introduction of U.S. students to interdisciplinary science and to the international research community through work at Hungarian institutions and direct involvement with the project's central network survey.