The study of network robustness is a critical tool in the characterization and sense making of
complex interconnected systems such as infrastructure, communication and social networks …

Controlling the dissemination of an entity (eg, meme, virus, etc) on a large graph is an
interesting problem in many disciplines. Examples include epidemiology, computer security …

We develop a theoretical framework for the study of epidemiclike social contagion in large
scale social systems. We consider the most general setting in which different communication …

The decrease of the spectral radius, an important characterizer of network dynamics, by
removing links is investigated. The minimization of the spectral radius by removing m links is …

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but
adversarially designed perturbations, known as adversarial examples. To address this …

The concept of network served for a long time as a metaphor supporting a structural
approach, ie, an approach that puts the accent on the relations among the constituents of a …

Improving robustness of complex networks is a challenge in several application domains,
such as power grids and water management networks. In such networks, high robustness …

In many political elections, the electorate appears to be a composite of partisan and
independent voters. Given that partisans are not likely to convert to a different party, an …

Patch-based denoising algorithms currently provide the optimal techniques to restore an
image. These algorithms denoise patches locally in “patch-space”. In contrast, we propose …

Recent progress in the areas of network science and control has shown a significant
promise in understanding and analyzing epidemic processes. A well-known model to study …