我们是属于弱势群体，像Nature/Science/PNAS这些期刊的论文很少关注我们的工作，所以还是比较高兴的。两篇文章分别引用了我08年的一篇《欧洲物理学快报》（EPL 82，28002）和07年的一篇《美国物理评论》（PRE 75，036106）。
第一篇是8月4号出版的，讲的是人类相互作用行为中的时间统计特性。特别地，他们发现人群的行为满足类似于经济学中的Gibrat定律（实际上和原来的定律有的不一样，是一种推广的形式），有趣的是，他们利用一种简单的方式把Gibrat定律和Hurst指数联系了起来。我个人觉得更有趣的问题是，他们提出了一个非常普适的问题，就是增长的网络系统是否也满足Gibrat定律。只要有记录了每一条边出现时间的网络系统，我们就可以验证或者推翻这个假想的定律。我准备找一些刚刚入门或正准备入门演化网络分析的研究生或青年教师做一下这方面的研究。我猜测他们从Zoltan Eisler08年的Adv. Phys.的论文中得到了相当的启发。去年Zoltan给我写信撺掇我验证人类行为是否满足Taylor定律，我答应了但是一直没有做，看样子也是可以做做的问题。这篇文章猜测群体相互作用是产生这个Gibrat定律的可能原因，我不认可这种猜测，我手头正好有完全没有相互作用的人类动力学数据（人和物品作用而非人和人作用），可以马上看看是否符合他们的猜测。
Scaling laws of human interaction activity
Abstract
Even though people in our contemporary technological society are depending on communication, our understanding of the underlying laws of human communicational behavior continues to be poorly understood. Here we investigate the communication patterns in 2 social Internet communities in search of statistical laws in human interaction activity. This research reveals that human communication networks dynamically follow scaling laws that may also explain the observed trends in economic growth. Specifically, we identify a generalized version of Gibrat's law of social activity expressed as a scaling law between the fluctuations in the number of messages sent by members and their level of activity. Gibrat's law has been essential in understanding economic growth patterns, yet without an underlying general principle for its origin. We attribute this scaling law to long-term correlation patterns in human activity, which surprisingly span from days to the entire period of the available data of more than 1 year. Further, we provide a mathematical framework that relates the generalized version of Gibrat's law to the long-term correlated dynamics, which suggests that the same underlying mechanism could be the source of Gibrat's law in economics, ranging from large firms, research and development expenditures, gross domestic product of countries, to city population growth. These findings are also of importance for designing communication networks and for the understanding of the dynamics of social systems in which communication plays a role, such as economic markets and political systems.
第二篇是8月12号在线可获取的，还没有正式出版。这篇文章讲的是公益和赢利性实体分布的密度和当地人口密度之间的标度关系。模型非常优雅，实证非常震撼，结果非常简洁，实在是一篇难得的好文章。我唯一有疑问的地方，就是模型允许实体自由移动（这是一个优化过程），但是很难想象学校或者医院能够自由移动。所以我在审稿意见中提到真实的系统可能是一种贪心优化（以前的不能动了，新的可以加到当前看来最优化的位置上去），而这种贪心优化和全局优化结果可能很不一样。所以我建议他们做新的实验。Hawoong给我的回复是有些实体关闭了，新的又开了，如果从一个很漫长的时间尺度来看，可以看作是一种移动；另外，Jaegon新做了数值实验，表明即便采用贪心优化，也能够勉勉强强出来类似的结果（当然，波动很大，非常勉强）。这个回复不算是非常满意，但是不能强求一篇文章解决一切的问题，所以说这个文章发表是没有任何问题的。我觉得这个问题还是没有完全搞清楚，因为模型很简单（不一定要用真实的美国的人口数据，可以试验一大组人造的人口数据，作为一个一般化的模型进行讨论），所以有时间也可能作为游戏玩玩。
Scaling laws between population and facility densities
Abstract
When a new facility like a grocery store, a school, or a fire station is planned, its location should ideally be determined by the necessities of people who live nearby. Empirically, it has been found that there exists a positive correlation between facility and population densities. In the present work, we investigate the ideal relation between the population and the facility densities within the framework of an economic mechanism governing microdynamics. In previous studies based on the global optimization of facility positions in minimizing the overall travel distance between people and facilities, it was shown that the density of facility D and that of population ρ should follow a simple power law D ∼ ρ2/3. In our empirical analysis, on the other hand, the power-law exponent α in D ∼ ρα is not a fixed value but spreads in a broad range depending on facility types. To explain this discrepancy in α, we propose a model based on economic mechanisms that mimic the competitive balance between the profit of the facilities and the social opportunity cost for populations. Through our simple, microscopically driven model, we show that commercial facilities driven by the profit of the facilities have α = 1, whereas public facilities driven by the social opportunity cost have α = 2/3. We simulate this model to find the optimal positions of facilities on a real U.S. map and show that the results are consistent with the empirical data.
