Classical and Quantum Random Walks to Identify Leaders in Criminal Networks

Abstract

Random walks simulate the randomness of objects, and are key instruments in various fields such as computer science, biology and physics. The counter part of classical random walks in quantum mechanics are the quantum walks. Quantum walk algorithms provide an exponential speedup over classical algorithms. Classical and quantum random walks can be applied in social network analysis, and can be used to define specific centrality metrics in terms of node occupation on single-layer and multilayer networks. In this paper, we applied these new centrality measures to three real criminal networks derived from an anti-mafia operation named Montagna and a multilayer network derived from them. Our aim is to (i) identify leaders in our criminal networks, (ii) study the dependence between these centralities and the degree, (iii) compare the results obtained for the real multilayer criminal network with those of a synthetic multilayer network which replicates its structure.

arXiv commentThis paper will be included in the COMPLEX NETWORKS 2022 conference proceedings to be published by Springer Verlag

References in corpus (12)

• Emergence of scaling in random networks
• A Quantum Random Walk Search Algorithm
• Exponential algorithmic speedup by quantum walk
• Random Walks: A Review of Algorithms and Applications
• Quantum Google in a Complex Network
• Disrupting Resilient Criminal Networks through Data Analysis: The case of Sicilian Mafia
• Knowledge Acquisition: A Complex Networks Approach
• Comparing the writing style of real and artificial papers
• Criminal Networks Analysis in Missing Data scenarios through Graph Distances
• From classical to quantum walks with stochastic resetting on networks
• Classical and quantum random-walk centrality measures in multilayer networks
• Multilayer Network Analysis: The Identification of Key Actors in a Sicilian Mafia Operation