Most techniques for inferring connections within a network are typically computationally intensive and time-consuming and only applicable to a limited number of situations. The technique developed by the MIT researchers can be applied to a wide variety of areas, from studying gene regulatory networks, to social and online networks and other information sciences, providing insight on disease pathways, social interactions and other diverse fields.
"The technique readily reveals how local interactions affect global behavior,” says Muriel Medard, co-author of the study and a professor in RLE. "It is a versatile analytical tool that can be applied to networks of arbitrary dimension, and fits in a more general framework of applying information theory and linear algebra techniques to real-world problems, with many possible modifications.”
“Introducing such a foundational operation on networks seems surprising in this day and age,” Kellis says. “However, network science is still a young field, and with new insights coming from diverse domains, we expect many such additional surprises to emerge with broad impact in our increasingly interconnected world.”