论文标题
基于基准的基于群集网络上相互作用粒子的平均场近似
Motif-based mean-field approximation of interacting particles on clustered networks
论文作者
论文摘要
图表上的相互作用粒子通常用于研究物理学中的磁性，流行病学中的疾病传播以及社会科学中的舆论动态。有关大图的此类系统的平均场近似值的文献仅限于无聚类的图表，该图通常是基于度和对的标准近似值，通常是合理准确的。在这里，我们提出了一个基于基序的平均场近似值，该近似值考虑了大型簇图中的高阶子图结构。从数值上讲，我们的方程式与现有方法失败的随机模拟一致。
Interacting particles on graphs are routinely used to study magnetic behaviour in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs is limited to cluster-free graphs for which standard approximations based on degrees and pairs are often reasonably accurate. Here, we propose a motif-based mean-field approximation that considers higher-order subgraph structures in large clustered graphs. Numerically, our equations agree with stochastic simulations where existing methods fail.
