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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]. Most settings assume we know the underlying graph and aim to study properties of the spread. Much work has been done in detection, where the goal is to decide whether or not there is indeed an infection. This problem is of importance in deciding whether or not a computer network is under attack, for instance, or whether a product gets sold through word-of-mouth or thanks to the advertisement campaign (or both). More specifically, the problem of source detection or obfuscation has been extensively studied. On the other side of the spectrum, both experimental and theoretical work has tackled the problem of modeling, predicting the growth, and controlling the spread of epidemics.

Learning Graphs from Noisy Epidemic Cascades