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arXiv 2204.04469physics.soc-ph
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稀疏随机网络上的修正Heider平衡

Modified Heider Balance on Sparse Random Networks

R. Masoumi, F. Oloomi, S. Sajjadi, A. H. Shirazi, G. R. Jafari

摘要:

标准平衡研究中符号随机网络的缺乏促使我们扩展标准平衡模型的哈密顿量。具有可调参数的随机网络适合于更好地理解标准平衡作为基础动力学的行为。此外,标准平衡模型在其原始形式中不允许在网络中保留紧张的三和弦。因此,最近研究了全连通符号网络上平衡模型的热行为。结果表明,该模型随温度的变化发生了一个突然的相变。综合考虑这两个问题,我们研究了Erdens-Rényi随机网络结构平衡模型的热行为。我们提供了一个平均场的解决方案的模型。我们观察到一阶相变与温度,稀疏和密集连接的网络。我们检测到两个转变温度,$T_{cold}$和$T_{hot}$,表征滞后回线。我们发现,随着网络稀疏性的增加,$T_{cold}$和$T_{hot}$都减少。但随着稀疏度减小$T_{hot}$的斜率大于减小$T_{cold}$的斜率。因此,滞后区域变得更窄,直到在一定的稀疏度下,它消失。我们提供了一个相图中的温度结密度平面观察亚稳/共存区的行为更准确。然后,我们证明我们的平均场结果与一系列的蒙特卡罗模拟。

英文摘要:

The lack of signed random networks in standard balance studies has prompted us to extend the Hamiltonian of the standard balance model. Random networks with tunable parameters are suitable for better understanding the behavior of standard balance as an underlying dynamics. Moreover, the standard balance model in its original form does not allow preserving tensed triads in the network. Therefore, the thermal behavior of the balance model has been investigated on a fully connected signed network recently. It has been shown that the model undergoes an abrupt phase transition with temperature. Considering these two issues together, we examine the thermal behavior of the structural balance model defined on Erdős-Rényi random networks. We provide a Mean-Field solution for the model. We observe a first-order phase transition with temperature, for both the sparse and densely connected networks. We detect two transition temperatures, $T_{cold}$ and $T_{hot}$, characterizing a hysteresis loop. We find that with increasing the network sparsity, both $T_{cold}$ and $T_{hot}$ decrease. But the slope of decreasing $T_{hot}$ with sparsity is larger than the slope of decreasing $T_{cold}$. Hence, the hysteresis region gets narrower, until, in a certain sparsity, it disappears. We provide a phase diagram in the temperature-tie density plane to observe the meta-stable/coexistence region behavior more accurately. Then we justify our Mean-Field results with a series of Monte-Carlo simulations.

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