Network Science | Marco Grassia

Network Science

Learning Multi-Stage Sparsification for Maximum Clique Enumeration

We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network analysis tasks. In each stage, our approach learns the characteristics …

Network robustness improvement via long-range links

Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks …

Climbing Ranking Position via Long-Distance Backlinks

The best attachment consists in finding a good strategy that allows a node inside a network to achieve a high rank. This is an open issue due to its intrinsic computational complexity and to the giant dimension of the involved networks. The ranking …

Exploiting Long Distance Connections to Strengthen Network Robustness

Network fault tolerance (also known as resilience or robustness) is becoming a highly relevant topic, expecially in real networks, where it is essential to know to what extent it is still working notwithstanding its failures. Different questions need …

Long Distance In-Links for Ranking Enhancement

Ranking is a widely used technique to classify nodes in networks according to their relevance. Increasing one’s rank is a desiderable feature in almost any context; several approaches have been proposed to achieve this goal by exploiting in-links …