Motif-based mean-field approximation of interacting particles on clustered networks
Motif-based mean-field approximation of interacting particles on clustered networks
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DOI：
10.1103/physreve.105.l042301
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发表时间：
2022-04-28
影响因子：
2.4
通讯作者：
Koeppl, Heinz
中科院分区：
文献类型：
--
作者：
Cui, Kai;KhudaBukhsh, Wasiur R.;Koeppl, Heinz
图上相互作用的粒子通常用于研究物理学中的磁行为、流行病学中的疾病传播以及社会科学中的观点动态。关于大型图的此类系统的平均场近似的文献通常仍然局限于特定的动力学，或者假设无簇图，其中基于度和对的标准近似通常相当准确。在这里，我们提出了一种基于主题的平均场近似，该近似考虑了大型聚类图中的高阶子图结构。在数值上，我们的方程与现有方法失败的随机模拟一致。
Interacting particles on graphs are routinely used to study magnetic behavior in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs typically remains limited to specific dynamics, or assumes 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.