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Diversity of individual mobility patterns and emergence of aggregated scaling laws

Xiao-Yong Yan, Xiao-Pu Han, Bing-Hong Wang & Tao Zhou

Scientific Reports 3, Article number: 2678 doi:10.1038/srep02678

Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level,while the aggregated displacement distribution follows a power law with an exponential cutoff. Given the constraint on total travelling cost, this aggregated scaling law can be analytically predicted by the mixture nature of human travel under the principle of maximum entropy. A direct corollary of such theory is that the displacement distribution of a single mode of transportation should follow an exponential law, which also gets supportive evidences in known data. We thus conclude that the travelling cost shapes the displacement distribution at the aggregated level.

全文链接：http://www.nature.com/srep/2013/130918/srep02678/full/srep02678.html

详细介绍请看周涛博文：《位置分析：个体多样性与群体标度律》

人类行为时空特性的统计力学

周涛，韩筱璞，闫小勇，杨紫陌，赵志丹，汪秉宏

电子科技大学学报，2013年42卷第7期，第481-540页，复杂性科学专栏。

人类行为的定量化分析，特别是时空统计规律的挖掘和建模，是当前统计物理与复杂性科学研究的热点。对人类行为的深入理解，有助于解释若干复杂的社会经济现象，并在舆情监控、疾病防治、交通规划、呼叫服务、信息推荐等方面产生应用价值。该文综述人类行为时间和空间特性方面的研究进展，内容包括人类行为时间特性的实证分析和建模，人类行为空间特性的实证分析和建模，以及人类行为统计分析的应用研究。该文还将评述当前研究存在的亮点和不足，指出若干亟待解决的重大理论和实际问题。

全文链接：http://www.xb.uestc.edu.cn/nature/index.php?p=item&item_id=1319

详细介绍还是看周涛博文：《人类时空动力学迄今为止最完整的综述》

Efficient Learning Strategy of Chinese Characters Based on Network Approach

Xiaoyong Yan, Ying Fan, Zengru Di, Shlomo Havlin, Jinshan Wu

PLoS ONE 8(8): e69745. doi:10.1371/journal.pone.0069745

We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

全文链接：http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0069745

详细介绍请看我之前的博文：《一篇论文被BBC Future报道》

感谢各位指导老师和合作者！