The authors then compared these two groups based on a variety of statistics: the percentage of the total calls that were outgoing, the count of different phone numbers they dialed, and how diverse the recipients of their calls were. The result of this analysis is that the power law group had the most “anomalous and extreme calling patterns,” according to the authors. And, in most cases, these are potential signs of trouble.

Some accounts showed a high frequency of outgoing calls, but only to a limited number of (or only one) target phone numbers. The authors inferred that they are “robot-based users.” Another cluster of accounts had a high frequency of outbound calls, but had an inordinate number of targets, and called them all with equal frequency. The authors suspect that these are sales accounts, or represent instances of phone-based frauds.

The authors suggest that their work provides “information valuable to both academics and practitioners, especially mobile telecom providers.” But they then go on to ignore the network providers and focus on academics. For them, the key message is that cell phone users are a diverse population, and shouldn’t be modeled as if they all follow patterns that fall on a simple power law curve. In fact, even among the users that showed a power law distribution, the value of the exponent that described the curve varied a great deal.

Could an actual cell provider use this information? Clearly, a scammer like the guy who tried to gain access to one of our writers’ computers will show one of the patterns seen here: lots of calls, almost all outgoing, and spread among a wide variety of contact numbers. Even if the phone company felt no ethical obligation to block the practice, it might still see it as a drain on its resources (provided the scammers have an unlimited calling plan). At the same time, there will be numbers that show the same pattern, but for legitimate reasons—automated appointment reminders from medical practices spring to mind.

So, sadly, although patterns like this could be a useful starting point for investigations, and could definitely serve as evidence if a scammer gets caught, they’re not going to be especially useful in creating an automated system that could shut down scammers and spammers.

PNAS, 2013. DOI: 10.1073/pnas.1220433110  (About DOIs).