摘要
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.
出处
《上海理工大学学报》
CAS
北大核心
2012年第2期103-117,共15页
Journal of University of Shanghai For Science and Technology
基金
国家自然科学基金资助项目(10975126
91024026)
安徽省高校自然科学研究重大项目基金资助项目(KJ2011ZD07)
高校博士点基金资助项目(20093402110032)
关键词
人类行为动力学
复杂网络
信息物理
human behavior dynamics
complex network
information physics