复杂时空社会网络的演化、建模及动力学研究

The mobility patterns of individuals and the contact sequences between two friends have significant influences on the topological properties of social networks, as well as its dynamics, such as disease spreading and social contagions. We try to explore the spatiotemporal evolution of complex social networks based on three mass datasets, including the logs of cellphone communication, wireless internet access, and MMORPG game. The theories and methods from statistical physics, complexity science, complex networks, and data mining will be employed to investigate the stylized facts of individuals' spatiotemporal behaviors and interacting patterns, such as the distribution of inter-call durations, the distribution of motion displacements, the statistical features of communication networks and contact networks, to list a few. The underlying mechanisms will be proposed according to the empirical stylized facts obtained from the mass datasets. Based on the empirical results and proposed mechanisms, a computational model will be constructed to simulate the spatiotemporal evolution of social networks. In order to develop efficient strategies for controlling dynamical processes under different situations, several computational experiments will be conducted to investigate the diffusive processes on the dynamical social networks which are incorporated with human spatiotemporal behaviors.

社会网络中节点具有空间移动性，节点的交互也有特定时间顺序，这一特性不仅会影响网络的拓扑结构，还会影响网络的动力学过程，如流行病传播、社会传染等。本项目申请拟利用手机通讯定位数据、无线上线定位数据和网络游戏玩家行为日志数据，同时从时间和空间两个维度研究复杂社会网络的演化动力学。本项目将运用统计物理、复杂性科学和复杂网络的理论和方法，结合海量数据挖掘技术，研究个体时空行为和个体交互作用的典型特征（如呼叫时间间隔分布、移动位移分布、通讯网络、移动网络、空间接触网络等），探索其中蕴含的标度率和普适性，提炼出解释数据典型特征的内在机制，深化人类行为动力学的机理分析，构建机理与数据相结合的可计算混合模型，实现基于数据本质的模型校准。本课题还将依托流行病传播和社会传染等背景问题，通过计算实验阐明基于个体时空行为的网络动力学传播机制，为特定情景下的网络动力学控制提供扎实的科学思想和技术手段。

利用手机通讯数据和网络游戏日志数据，本项目运用统计物理、复杂性科学和复杂网络的理论和方法，对复杂社会网络的演化动力学进行了深入研究，重点关注了社会网络结构演化和网络节点行为动力学。研究成果加深了我们对手机用户的呼叫行为动力学、手机用户通讯网络演化特征、以及在线用户合网络形成偏好和局部结构形成机制的认识，促进了我们对用户交互行为规律和社会网络结构演化的理解，同时，进一步深化了我们对用户行为规律和网络结构形成的机理研究。本项目共计完成标注论文7篇，其中6篇已经发表，且被SCI收录，相关成果发表于PNAS, Scientific Reports, New Journal of Physics和Journal of Statistical Mechanism: Theory and Experiment.