As the world becomes more and more interconnected, our everyday objects become part of the Internet of Things, and our lives get more and more mirrored in virtual reality, where every piece of information, including misinformation, fake news and malware, can spread very fast practically anonymously. To suppress such uncontrolled spread, efficient computer systems and algorithms capable to track down such malicious information spread have to be developed. Currently, the most effective methods for source localization are based on sensors which provide the times at which they detect the spread. We investigate the problem of the optimal placement of such sensors in complex networks and propose a new graph measure, called Collective Betweenness, which we compare against four other metrics. Extensive numerical tests are performed on different types of complex networks over the wide ranges of densities of sensors and stochasticities of signal. In these tests, we discovered clear difference in comparative performance of the investigated optimal placement methods between real or scale-free synthetic networks versus narrow degree distribution networks. The former have a clear region for any given method’s dominance in contrast to the latter where the performance maps are less homogeneous. We find that while choosing the best method is very network and spread dependent, there are two methods that consistently stand out. High Variance Observers seem to do very well for spread with low stochasticity whereas Collective Betweenness, introduced in this paper, thrives when the spread is highly unpredictable.

随着世界变得越来越互联，我们的日常物品已成为物联网的一部分，我们的生活也越来越多地反映在虚拟现实中，在虚拟现实中，每条信息（包括错误信息，虚假新闻和恶意软件）都可以非常迅速地传播。几乎是匿名的。为了抑制这种不受控制的传播，必须开发能够跟踪这种恶意信息传播的有效计算机系统和算法。当前，用于源定位的最有效方法是基于传感器，该传感器提供它们检测扩散的时间。我们调查了此类传感器在复杂网络中的最佳放置问题，并提出了一种称为“集体中间性”的新图形测度，并将其与其他四个指标进行了比较。在传感器密度和信号随机性的广泛范围内，对不同类型的复杂网络进行了广泛的数值测试。在这些测试中，我们发现在实测或无标度的合成网络与窄度分布网络之间，所研究的最佳放置方法的比较性能存在明显差异。与后者相比，前者在任何给定方法的优势中都有明确的区域，后者的性能图不太均匀。我们发现，尽管选择最佳方法非常依赖于网络和扩展，但是有两种方法始终脱颖而出。高方差观察者似乎对随机性较低的价差表现很好，而本文介绍的集体中间性在价差高度不可预测时会蓬勃发展。