In the past 10 years, researchers have learned a great deal about the organization of such networks, in particular their topology the patterns of connections between different points, or nodes, in the network. Slotine and his colleagues applied traditional control theory to these recent advances, devising a new model for controlling complex, self-assembling networks.
The researchers started by devising a new computer algorithm to determine how many nodes in a particular network need to be controlled in order to gain control of the entire network. (Examples of nodes include members of a social network, or single neurons in the brain.)
"The obvious answer is to put input to all of the nodes of the network, and you can, but that's a silly answer," Slotine says. "The question is how to find a much smaller set of nodes that allows you to do that."
There are other algorithms that can answer this question, but most of them take far too long years, even. The new algorithm quickly tells you both how many points need to be controlled, and where those points known as "driver nodes" are located.
Next, the researchers figured out what determines the number of driver nodes, which is unique to each network. They found that the number depends on a property called "degree distribution," which describes the number of connections per node.
A higher average degree (meaning the points are densely connected) means fewer nodes are needed to control the entire network. Sparse networks, which have fewer connections, are more difficult to control, as are networks where the node degrees are highly variable.
In future work, Slotine and his collaborators plan to delve further into biological networks, such as those governing metabolism. Figuring out how bacterial metabolic networks are controlled could help biologi
|Contact: Caroline McCall|
Massachusetts Institute of Technology