Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, D-14195 Berlin, Germany.
Chaos. 2022 Jul;32(7):073108. doi: 10.1063/5.0090114.
The investigation of dynamical processes on networks has been one focus for the study of contagion processes. It has been demonstrated that contagions can be used to obtain information about the embedding of nodes in a Euclidean space. Specifically, one can use the activation times of threshold contagions to construct contagion maps as a manifold-learning approach. One drawback of contagion maps is their high computational cost. Here, we demonstrate that a truncation of the threshold contagions may considerably speed up the construction of contagion maps. Finally, we show that contagion maps may be used to find an insightful low-dimensional embedding for single-cell RNA-sequencing data in the form of cell-similarity networks and so reveal biological manifolds. Overall, our work makes the use of contagion maps as manifold-learning approaches on empirical network data more viable.
网络动力学过程的研究一直是传染病过程研究的重点之一。已经证明，传染病可用于获取有关节点在欧几里得空间中嵌入的信息。具体来说，可以使用阈值传染病的激活时间来构建传染病图作为流形学习方法。传染病图的一个缺点是计算成本高。在这里，我们证明了对阈值传染病的截断可以大大加快传染病图的构建速度。最后，我们表明，传染病图可用于以细胞相似性网络的形式为单细胞 RNA 测序数据找到有见地的低维嵌入，从而揭示生物流形。总的来说，我们的工作使得在经验网络数据上使用传染病图作为流形学习方法更加可行。
