Opinion dynamics on tie-decay networks
Opinion dynamics on tie-decay networks
复制标题
关于关系衰减网络的意见动态
DOI：
10.1103/physrevresearch.3.023249
复制
发表时间：
2021
影响因子：
4.2
通讯作者：
Masuda, Naoki
中科院分区：
文献类型：
--
作者：
Sugishita, Kashin;Porter, Mason A.;Beguerisse-Díaz, Mariano;Masuda, Naoki
在社交网络中，交互模式通常会随着时间而变化。我们研究关系衰减网络的观点动态，其中关系强度在存在交互时立即增加，并在交互之间呈指数衰减。具体来说，我们在这些联系衰减网络上制定了连续时间拉普拉斯动力学和离散时间观点动力学 DeGroot 模型，并对连续时间拉普拉斯动力学进行了数值计算。我们通过研究束缚衰减网络的组合拉普拉斯矩阵的谱间隙来检查收敛速度。首先，我们将根据经验数据构建的束缚衰减网络的拉普拉斯矩阵的谱间隙与相应的随机和聚合网络的谱间隙进行比较。我们发现经验网络的谱间隙往往小于随机网络和聚合网络的谱间隙。其次，我们研究光谱间隙随衰减率和时间的变化。直观上，我们预计较小的衰减率会导致快速收敛，因为对于较小的衰减率，两个节点之间每次相互作用的影响持续时间较长。此外，随着时间的推移和更多交互的发生，我们预计最终会趋同。然而，我们证明光谱间隙不需要随衰减率单调减小或随时间单调增加。我们的结果强调了时间网络中边缘增强和衰减时间之间相互作用的重要性。
In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks.
通过设计链接顺序来加速时间网络中的协调
影响因子：
4.6
作者：
N. Masuda
通讯作者：
N. Masuda
DOI：
--
发表时间：
2021
期刊：
bioRxiv
影响因子：
--
作者：
Valeria Gelardi;A. Barrat;N. Claidière
通讯作者：
N. Claidière
具有领带衰减、扩散和对流的连续时间网络模型
影响因子：
2.4
作者：
Xinzhe Zuo;M. Porter
通讯作者：
M. Porter