To present our use case, let’s use a common scenario: in a residential neighborhood, a store robbery is committed during the day by a group of four criminals. The criminals are masked, they use a stolen vehicle and leave no fingerprints. In that kind of case, finding an answer may take a lot of legwork.
Equipped with a search warrant, law enforcement can contact mobile phone operators to collect information about the calls made and received near the robbery when it happened.
From there, the first step for investigators wanting to leverage graph technology, is to model the data as a graph. The data, phone operators provide law enforcement with, is often tabular (a list) but inherently, phone record data constitute a graph, or a network, of devices linked together via calls. For years, investigators had to work with this data as tables and rows because the technology in use, relational databases, was built that way. Trying to identify unique phone numbers and their relationships from a spreadsheet for instance is tedious. Instead, graph technology allows us to work with the data in its natural form.