Emergence of encounter networks due to human mobility

There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times. This generates a temporal network that is characterized by global quantities. We compare this dynamics with real data for two cities: New York City and Tokyo. We use data from the location-based social network Foursquare and obtain the emergent temporal encounter network, for these two cities, that we compare with our model. We found long-range (Lévy-like) distributions for traveled distances and time intervals that characterize the emergent social network due to human mobility. Studying this connection is important for several fields like epidemics, social influence, voting, contagion models, behavioral adoption and diffusion of ideas.当前学界针对人类移动性与偶遇网络的研究已蔚然成风，但这两大重要领域之间的关联研究却直至近期才刚刚起步。二者的紧密关联显而易见：人类移动性催生偶遇行为，而此类偶遇又可能引发传染现象，甚至促成友谊的建立。我们构建了一组随机游走者的模型，这些游走者遵循类似列维飞行（Lévy flights）的策略在空间中造访各类地点。我们对空间与时间维度上的偶遇行为进行量化，并在多次偶遇的游走者之间建立关联，由此生成一个以全局特征为表征的时序网络。我们将该动态模型与纽约市、东京市两座城市的真实数据进行对比。我们依托基于位置的社交网络Foursquare的数据集，获取了这两座城市的涌现性时序偶遇网络，并将其与我们的模型进行比对。我们发现，由人类移动性催生的涌现性社交网络，其出行距离与时间间隔均呈现长程类列维分布特征。对这一关联展开研究，可对流行病学、社会影响力、选举、传染模型、行为采纳与思想传播等诸多领域产生重要价值。