论文标题
在复杂网络中找到相互作用信号的来源
Locating the source of interacting signal in complex networks
论文作者
论文摘要
我们研究了在复杂网络中找到自相互作用信号传播源的问题。我们使用众所周知的谣言模型作为自我交流过程的一个例子。根据基于SIR流行动力学的模型，感染的节点可能会相互作用，并互相劝阻，彼此闲聊概率$α$。我们比较了来源定位的三种算法：有限的Pinto-Thiran-Vettarli（LPTV），梯度最大似然（GMLA）和一种基于时间和距离之间的Pearson相关性。数值模拟的结果表明，感染节点之间的其他相互作用降低了LPTV和Pearson的质量。 GMLA是对自身互动的有害影响的最具耐药性，当扩散率低于0.5时，对于中等和高水平的随机性，这对于中等和高水平的随机性尤其可见。原因可能是GMLA仅使用最近的观察者，而这些观察者受感染节点之间相互作用的影响的可能性要小得多，因为随着流行病的发展和感染剂的数量，这些接触变得很重要。
We investigate the problem of locating the source of a self-interacting signal spreading in a complex networks. We use a well-known rumour model as an example of the process with self-interaction. According to this model based on the SIR epidemic dynamics, the infected nodes may interact and discourage each other from gossiping with probability $α$. We compare three algorithms of source localization: Limited Pinto-Thiran-Vettarli (LPTV), Gradient Maximum Likelihood (GMLA) and one based on Pearson correlation between time and distance. The results of numerical simulations show that additional interactions between infected nodes decrease the quality of LPTV and Pearson. GMLA is the most resistant to harmful effects of the self-interactions, which is especially visible for medium and high level of stochasticity of the process, when spreading rate is below 0.5. The reason for this may be the fact that GMLA uses only the nearest observers, which are much less likely affected by the interactions between infected nodes, because these contacts become important as the epidemics develops and the number of infected agents increases.
