具有间接同位链接的图模型
A Graph Model with Indirect Co-location Links
摘要：
图模型被广泛用于分析嵌入在社会联系中的扩散过程和开发应用程序。一系列图形模型可用于逼真地复制底层社会结构和动态。然而，目前大多数图模型只能考虑协同定位交互网络中个体之间的并发交互。然而，他们没有考虑间接的相互作用，可以传播的项目，以个人谁访问同一地点在不同的时间，但在一定的时间限制。通过直接和间接相互作用发生的扩散现象称为同地异时（SPDT）扩散。本文介绍了一种模型，以合成共同定位的互动图捕捉直接的互动，其中个人满足在一个位置，和间接的互动，其中个人访问同一地点在不同的时间在一个设定的时间范围内。我们分析了来自社交网络应用程序的200万用户的6000万次位置更新，以表征图形属性，包括时空相关性及其时间演化特征，例如突发或持续行为。生成的合成图再现了现实接触图的扩散动态，与其他接触图模型相比，预测误差降低了高达82%，证明了其预测流行病传播的潜力。
英文摘要：
Graph models are widely used to analyse diffusion processes embedded in social contacts and to develop applications. A range of graph models are available to replicate the underlying social structures and dynamics realistically. However, most of the current graph models can only consider concurrent interactions among individuals in the co-located interaction networks. However, they do not account for indirect interactions that can transmit spreading items to individuals who visit the same locations at different times but within a certain time limit. The diffusion phenomena occurring through direct and indirect interactions is called same place different time (SPDT) diffusion. This paper introduces a model to synthesize co-located interaction graphs capturing both direct interactions, where individuals meet at a location, and indirect interactions, where individuals visit the same location at different times within a set timeframe. We analyze 60 million location updates made by 2 million users from a social networking application to characterize the graph properties, including the space-time correlations and its time evolving characteristics, such as bursty or ongoing behaviors. The generated synthetic graph reproduces diffusion dynamics of a realistic contact graph, and reduces the prediction error by up to 82% when compare to other contact graph models demonstrating its potential for forecasting epidemic spread.
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