Social and place-focused communities in location-based online social networks
Social and place-focused communities in location-based online social networks
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DOI：
10.1140/epjb/e2013-40253-6
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发表时间：
2013-06-01
影响因子：
1.6
通讯作者：
Mascolo, Cecilia
中科院分区：
文献类型：
--
作者：
Brown, Chloe;Nicosia, Vincenzo;Mascolo, Cecilia
由于普遍存在的廉价互联网接入和智能手机的无处不在，世界各地现在有数百万人使用基于位置的在线社交网络服务。了解这些系统的结构属性以及它们对用户习惯和移动性的依赖具有许多潜在的应用，包括资源推荐和链接预测。在这里，我们通过使用从流行的基于位置的在线社交服务中收集的关于已声明的社会关系和用户对物理地点的访问的纵向信息来构建和表征社交和地点聚焦图表。我们发现，虽然社交和地点聚焦图是从相同的数据集构建的，但它们具有完全不同的结构属性。我们发现，社交和位置聚焦图具有不同的全局和中观结构，特别是社交社区和地点聚焦社区的重叠可以忽略不计。因此，仅在社交图上执行的基于社区检测的群组推理不能分离出以地点为中心的群组，即使这些群组确实存在于网络中。通过研究社区内部TIE结构的演化，我们发现聚集位置数据的时间段对以地点为中心的社区的稳定性有很大影响，并且关于基于地点的群体的信息对于以用户为中心的应用程序可能比仅从社会社区分析获得的信息更有用。
Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.