Complex networks model the structure and function of critical technological, biological, and communication systems. Network dismantling---the targeted removal of nodes to fragment a network---is essential for analyzing and improving system …
In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for astrophysics, as it can provide insights into the evolution of the star …
Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have been used in a wide variety of application domains. Surprisingly, over the past two decades, graphs in general-and …
The robustness of networks plays a crucial role in various applications. Network dismantling, the process of strategically removing nodes or edges to maximize damage, is a known NP-hard problem. While heuristics for node removal exist, edge network …
Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we introduce CoreGDM, a trainable algorithm for network dismantling via node-removal. The approach is based on …
In this paper we propose a method to reduce the running time to solve the Maximum Clique Enumeration (MCE) problem. Specifically, given a network we employ geometric deep learning in order to find a simpler network on which running the algorithm to …