Ricerc@Sapienza

Graphs are pervasive in various applications capturing the complex behavior observed in biological, financial, and social networks, to name a few. Two major learning tasks over graphs are topology identification and inference of signals evolving over graphs. Existing approaches typically aim at identifying the topology when signals on all nodes are observed, or, recovering graph signals over networks with known topologies. In practice however, signal or graph perturbations can be present in both tasks, due to model mismatch, outliers, outages or adversaries.

Graphs are pervasive in different fields unveiling complex relationships between data. Two major graph-based learning tasks are topology identification and inference of signals over graphs. Among the possible models to explain data interdependencies, structural equation models (SEMs) accommodate a gamut of applications involving topology identification. Obtaining conventional SEMs though requires measurements across nodes. On the other hand, typical signal inference approaches “blindly trust” a given nominal topology.