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Machine Learning Partners in Criminal Networks. (arXiv:2209.03171v1 [physics.soc-ph])
stat.ML updates on arXiv.org arxiv.org
Recent research has shown that criminal networks have complex organizational
structures, but whether this can be used to predict static and dynamic
properties of criminal networks remains little explored. Here, by combining
graph representation learning and machine learning methods, we show that
structural properties of political corruption, police intelligence, and money
laundering networks can be used to recover missing criminal partnerships,
distinguish among different types of criminal and legal associations, as well
as predict the total amount of money exchanged …
arxiv machine machine learning networks partners physics soc