Learning Graphs from Noisy Epidemic Cascades

Abstract

Epidemic models accurately represent (among other processes) the spread of diseases, information (rumors, viral videos, news stories, etc.), the spread of malevolent agents in a network (computer viruses, malicious apps, etc.), or even biological processes (pathways in cell signaling networks, chains of activation in the gene regulatory network, etc.). We focus on epidemics that spread on an underlying graph [5].