As the world becomes increasingly connected, the need to ensure the proper functioning of its many underlying¹ networks -- such as the Internet, power grids², global air transportation and ecological³ networks -- also is increasing. But controlling networks is very difficult. Now a Northwestern University research team has developed the first broadly applicable computational approach identifying interventions⁴ that can both rescue complex networks from the brink⁵（边缘） of failure and reprogram them to a desired task.

 

"A fundamental property of networks is that a perturbation（扰乱，不安） to one node can affect other nodes, potentially causing the entire system to change behavior or fail," said Adilson E. Motter, who supervised the research. "We have turned this principle on its head for something positive: to control network behavior. This novel approach to control could have a transformative impact on the field of complex networks."

 

Motter is the Harold H. and Virginia Anderson Professor of Physics and Astronomy at Northwestern's Weinberg College of Arts and Sciences.

 

In a demonstration⁶ of its broad applicability, Motter and his colleagues used their framework both to mitigate⁷ cascading⁸ failures in a power-grid network and to identify potential drug targets in a biochemical signaling network of human cancer.

 

The findings are published in the journal Nature Communications.

 

The same connections that provide functionality in networks also can serve as conduits for the propagation of failures and instabilities, Motter said. The emergence⁹ of global air transportation and computer networks, for example, brings obvious benefits but at the price of facilitating the spread of diseases and malware.

 

Furthermore, ecological networks are increasingly affected¹⁰ by perturbations stemming from human actions, and a growing number of human diseases are being linked to malfunction¹¹ of cellular¹² and molecular¹³ networks.

 

Networks defy human control, however, even in the simplest cases, not only because complex networks consist of a large number of complicatedly connected parts but mainly because they respond nonlinearly（非线性） to disturbances¹⁵: A small disturbance¹⁴ can create a disproportionately large problem.

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