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

美国的目标是-匈牙利卡拉马祖学院的扬·托博奇尼克和布达佩斯埃奥特沃斯大学的乔治·坎皮斯合作的研究项目是研究社交网络的结构和动力。 他们将开始一个简单的模型的形成和增长的社会网络，包括个人节点的属性或特点，影响他们如何与其他节点连接。 反过来，研究人员将把他们的抽象模型与通过对学生的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比例。 结果应改善新兴网络的属性的定义，特别是网络的聚类和层次结构，通过观察某些物理特性，如从许多集群到一个单一的集群作为控制参数的函数相变。移除随机节点的影响和一些基于博弈的效率测量观察也将被考虑。 总的来说，新的跨学科方法应加强目前的社会科学工作的网络，通过美国，匈牙利专家的贡献，在系统建模，认知机制和计算数学。 结果预计将改善目前的方法，检查真实的实体，如人，组织和社区之间的关系。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联合收割机互补人才，并在共同感兴趣和能力强的领域共享研究资源，实现了推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工作和直接参与该项目的中央网络调查，将美国学生引入跨学科科学和国际研究界。

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.