Title: Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks Show Chinese title

Title: 传播事件和投影网络在捕捉多部分网络上动态的局限性

Abstract: Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks. Show Chinese abstract

Abstract: 许多具有科学兴趣的系统可以被概念化为多部分网络。 示例包括性传播感染的传播、科学合作、人类友谊、产品推荐系统和代谢网络。 在实践中，这些系统通常在投影到单一类别的节点后进行研究，从而丢失了关键信息。 在这里，我们通过比较时间多部分网络上的传播动力学与其时间聚合的单一部分投影来填补这一重要知识空白，以确定丢失的信息对我们预测系统动力学能力的影响。 我们表明，在三个层次上，传播模型的动力学在多部分网络和其投影上可能有显著的不同：最终结果、从一次实现到另一次实现的变异性大小以及时间轨迹的整体形状。 我们发现，在时间聚合尺度上，节点数量与活跃边数量的比率决定了投影网络捕捉多部分网络动力学的能力。 最后，我们探讨了多部分网络的哪些特性在生成更能再现真实多部分网络中观察到的动力学行为的合成网络时是至关重要的。