Tomorrow is Today: Algorithm Tracks Epidemics, Crime Back to the...

12:23 17th Aug 2012

Notes: 59

Tags: Technology Tech Computers Computer Science Computer Modeling Networks Network Analysis Pre-Crime Surveillance Law Enforcement Epidemiology

$Algorithm Tracks Epidemics, Crime Back to the Source Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees. In the Internet age, however, the networks used by organized criminals have changed. Innumerable nodes and connections escalate the complexity of these networks, making it ever more difficult to root out the guilty party. EPFL researcher Pedro Pinto of the Audiovisual Communications Laboratory and his colleagues have developed an algorithm that could become a valuable ally for investigators, criminal or otherwise, as long as a network is involved. The team’s research was published in the journal Physical Review Letters. “Using our method, we can find the source of all kinds of things circulating in a network just by ‘listening’ to a limited number of members of that network,” explains Pinto. Suppose you come across a rumor about yourself that has spread on Facebook and been sent to 500 people – your friends, or even friends of your friends. How do you find the person who started the rumor? “By looking at the messages received by just 15–20 of your friends, and taking into account the time factor, our algorithm can trace the path of that information back and find the source,” Pinto adds. This method can also be used to identify the origin of a spam message or a computer virus using only a limited number of sensors within the network. Out in the real world, the algorithm can be employed to find the primary source of an infectious disease, such as cholera. “We tested our method with data on an epidemic in South Africa provided by EPFL professor Andrea Rinaldo’s Ecohydrology Laboratory,” says Pinto. “By modeling water networks, river networks and human transport networks, we were able to find the spot where the first cases of infection appeared by monitoring only a small fraction of the villages.” Read more: http://www.laboratoryequipment.com/news/2012/08/algorithm-tracks-epidemics-crime-back-source (ht laboratoryequipment)$

Algorithm Tracks Epidemics, Crime Back to the Source

Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees.

In the Internet age, however, the networks used by organized criminals have changed. Innumerable nodes and connections escalate the complexity of these networks, making it ever more difficult to root out the guilty party. EPFL researcher Pedro Pinto of the Audiovisual Communications Laboratory and his colleagues have developed an algorithm that could become a valuable ally for investigators, criminal or otherwise, as long as a network is involved. The team’s research was published in the journal Physical Review Letters.

“Using our method, we can find the source of all kinds of things circulating in a network just by ‘listening’ to a limited number of members of that network,” explains Pinto. Suppose you come across a rumor about yourself that has spread on Facebook and been sent to 500 people – your friends, or even friends of your friends. How do you find the person who started the rumor? “By looking at the messages received by just 15–20 of your friends, and taking into account the time factor, our algorithm can trace the path of that information back and find the source,” Pinto adds. This method can also be used to identify the origin of a spam message or a computer virus using only a limited number of sensors within the network.

Out in the real world, the algorithm can be employed to find the primary source of an infectious disease, such as cholera. “We tested our method with data on an epidemic in South Africa provided by EPFL professor Andrea Rinaldo’s Ecohydrology Laboratory,” says Pinto. “By modeling water networks, river networks and human transport networks, we were able to find the spot where the first cases of infection appeared by monitoring only a small fraction of the villages.”

(ht laboratoryequipment)