Topological Properties and Temporal Dynamics of Place Networks in Urban Environments
Topological Properties and Temporal Dynamics of Place Networks in Urban Environments
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DOI：
10.1145/2740908.2745402
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发表时间：
2015-02
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影响因子：
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通讯作者：
A. Noulas;B. Shaw;R. Lambiotte;C. Mascolo
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文献类型：
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作者：
A. Noulas;B. Shaw;R. Lambiotte;C. Mascolo
了解由移动的用户的轨迹形成的空间网络可以有益于从流行病学到本地搜索的应用。尽管在许多领域都有潜在的影响，但由于缺乏具有良好时空分辨率的大规模数据，人类移动网络的几个方面在很大程度上仍未得到探索。我们使用基于位置的服务Foursquare的纵向数据集，对地点网络的拓扑特性进行了实证分析，并注意到它们与在线社交网络在重尾度分布，三元闭合机制和小世界属性方面的相似之处。然而，与社交网络不同的是，地点网络在可扩展性方面呈现出与网络图惊人相似的连接趋势。我们利用额外的语义信息来解释节点如何承担功能角色，如“旅行枢纽”，或“食物点”在这些网络中的行为。最后，由于大量的新链接出现在地方网络随着时间的推移，我们制定了经典的链接预测问题在这个新的领域。我们提出了一种新的引力模型变体，它汇集了城市环境中场所间连通性的三个基本要素：网络级交互、人类流动动力学和地理距离。我们评估了这个模型，发现它在预测100个热门城市样本中的新链接的任务上优于许多基线预测器和监督学习算法。
Understanding the spatial networks formed by the trajectories of mobile users can be beneficial to applications ranging from epidemiology to local search. Despite the potential for impact in a number of fields, several aspects of human mobility networks remain largely unexplored due to the lack of large-scale data at a fine spatiotemporal resolution. Using a longitudinal dataset from the location-based service Foursquare, we perform an empirical analysis of the topological properties of place networks and note their resemblance to online social networks in terms of heavy-tailed degree distributions, triadic closure mechanisms and the small world property. Unlike social networks however, place networks present a mixture of connectivity trends in terms of assortativity that are surprisingly similar to those of the web graph. We take advantage of additional semantic information to interpret how nodes that take on functional roles such as 'travel hub', or 'food spot' behave in these networks. Finally, motivated by the large volume of new links appearing in place networks over time, we formulate the classic link prediction problem in this new domain. We propose a novel variant of gravity models that brings together three essential elements of inter-place connectivity in urban environments: network-level interactions, human mobility dynamics, and geographic distance. We evaluate this model and find it outperforms a number of baseline predictors and supervised learning algorithms on a task of predicting new links in a sample of one hundred popular cities.