论文标题
确定人接触网络中致密和稀疏的时间动态
Identifying the temporal dynamics of densification and sparsification in human contact networks
论文作者
论文摘要
人类互动的时间社交网络在理解人类行为的基本模式方面是巨大的。在这些网络中，相互作用发生在个人（即节点）之间，他们在不同时间相互连接，最终导致具有动态组成的复杂系统范围的Web。网络中的动态行为不仅发生在本地，而且发生在全球层面上，因为系统扩展或收缩是由于：节点群体大小的变化或两个节点之间连接的机会变化。在这里，我们提出了一种数值最大样本方法来估计人口大小和在任何给定时间点连接两个节点的概率。该方法的一个优点是它仅依赖于综合数量，这些数量易于访问并摆脱隐私问题。我们的方法使我们能够确定每种机制在人类接触的致密和稀疏中的同时（而不是异步）的贡献，从而更好地了解了人类如何共同构建和解构社交网络。
Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.
