Optimizing sensors placement in complex networks for localization of hidden signal source: A review

Abstract

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 Betwenness, introduced in this paper, thrives when the spread is highly unpredictable.

arXiv comment28 pages, 18 figures, 11 tables

References in corpus (13)

• Statistical mechanics of complex networks
• Epidemic spreading in scale-free networks
• Epidemic processes in complex networks
• The spread of epidemic disease on networks
• Self-similar community structure in organisations
• The spread of low-credibility content by social bots
• What's in a crowd? Analysis of face-to-face behavioral networks
• Locating the Source of Diffusion in Large-Scale Networks
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Anatomy of an online misinformation network
• The statistical mechanics of complex signaling networks : nerve growth factor signaling
• Locating the source of diffusion in complex networks by time-reversal backward spreading
• Multiple propagation paths enhance locating the source of diffusion in complex networks

Cited by in corpus (6)

• Sequential Attention Source Identification Based on Feature Representation
• Detecting Hidden Layers from Spreading Dynamics on Complex Networks
• Discovering hidden layers in quantum graphs
• Source location on multilayer networks
• Locating the source of interacting signal in complex networks
• Evolutionary optimization of spatially-distributed multi-sensors placement for indoor surveillance environments with security levels