RECAST: telling apart social and random relationships in dynamic networks
RECAST: telling apart social and random relationships in dynamic networks
复制标题
DOI：
10.1145/2507924.2507950
复制
发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Pedro O. S. Vaz de Melo;A. C. Viana;M. Fiore;K. Jaffrès-Runser;Frédéric Le Mouël;A. Loureiro
中科院分区：
文献类型：
--
作者：
Pedro O. S. Vaz de Melo;A. C. Viana;M. Fiore;K. Jaffrès-Runser;Frédéric Le Mouël;A. Loureiro
在本文中，我们认为，在动态网络中准确发现随机和社会关系的能力对于依赖于人类例程的网络应用程序至关重要，例如，机会路由。因此，我们提出了一种策略来分析用户在移动网络中的互动，在移动网络中，用户根据他们的兴趣和活动动态采取行动。我们的策略，称为随机关系分类器策略（RECAST），允许对用户的无线交互进行分类，将随机交互从不同类型的社会关系中分离出来。为此，RECAST观察了真实系统与实体决策完全随机的等效系统的不同之处。我们评估了RECAST分类在不同网络环境中收集的真实用户联系数据集上的有效性。我们的分析揭示了数据集中用户无线交互动态的显著差异，我们利用这些数据揭示了社会关系对机会路由的影响。
In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to network applications that rely on human routines, such as, e.g., opportunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from different kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing.