Prof. Sujay Sanghavi and Prof. Sanjay Shakkottai of UT ECE have received a National Science Foundation (NSF) award for their work on "NeTS: Small: Inverse Problems from Cascades: Structure, Causation and Opinions." Professors Sanghavi and Shakkottai aim to develop a new theoretical and algorithmic understanding of these classic processes. Their  focus is on using cascades as an inference and learning tool, instead of merely a mathematically convenient model. In particular, the aim is to ascertain important but hidden network structure and properties, from partial and very noisy observations of cascade progressions on it. For example, it will enable the learning of the network on which diseases spread in society, based primarily on the information of when people get sick (but without information on who infected whom).

Cascades are network phenomena where the activation/failure of one node increases the likelihood of activation of its neighbors; this results in an event starting at one node eventually affecting a much larger part of the network via successive spread. Cascade processes severe as flexible yet coherent models for several phenomena: spread of viruses and malware in mobile phones, diseases in human society, opinions and actions in online social networks, and failures in power and transportation grids.

This "inverse" view - of learning structure from process -  runs counter to the vast majority of work on cascades, which is focused on the “forward" problem (of predicting how a cascade will spread given network properties). The award is slated for $500k for three years, and will both support and leverage ECE's multi-pronged efforts into network science and data analytics.