人类行为、复杂网络及信息挖掘的统计物理研究

通过实证统计与理论模型分析相结合对复杂系统进行研究是一种全新的认识和探索.建议从人类行为的统计特性、复杂网络同步与复杂神经网络、信息挖掘与复杂网络链路预测3个方面,基于大量的实证统计和分析,结合有效的动力学模型,针对人类自身行为的规律特性、社会个体之间的相互作用、神经系统的动力学演化、信息的有效推荐和网络演化的有效预测等重要问题,运用统计物理理论进行全方位的探索,深入挖掘各种决定复杂系统演化过程的基本机制与规律.

Integrating empirical statistics and theoretical models is a novel and promising way to study complex systems. This survey summarized recent progress on human dynamics, complex networks and information filtering, suggesting that to apply the perspectives and methods from statistical physics based on extensive empirical data and build effective dynamical models may solve some long-standing challenges, such as uncovering the hidden regularities of human behavior, revealing the rules governing the interactions between social individuals, characterizing the dynamical evolution of neural systems, digging out personalized tastes, predicting missing information, and so on.