Marco
Grassia
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Latent Geometry-Driven Network Automata for Complex Network Dismantling
Complex networks model the structure and function of critical technological, biological, and communication systems. Network …
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Saliency Matters: From Nodes to Objects
Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have …
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Machine Learning Supernovae’s Progenitor Characterization
In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their …
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Edge Dismantling with Geometric Reinforcement Learning
The robustness of networks plays a crucial role in various applications. Network dismantling, the process of strategically removing …
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Robustness and resilience of complex networks
Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected …
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Learning fine-grained search space pruning and heuristics for combinatorial optimization
Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are …
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CoreGDM: Geometric Deep Learning Network Decycling and Dismantling
Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we …
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Geometric Deep Learning Graph Pruning to Speed-Up the Run-Time of Maximum Clique Enumerarion Algorithms
In this paper we propose a method to reduce the running time to solve the Maximum Clique Enumeration (MCE) problem. Specifically, given …
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Network Topology to Predict Bibliometrics Indices: A Case Study
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Link Prediction in Time Varying Social Networks
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Insights into countries’ exposure and vulnerability to food trade shocks from network-based simulations
In the context of a global food system, the dynamics associated to international food trade have become key determinants of food …
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Efficient Node PageRank Improvement via Link Building using Geometric Deep Learning
Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, …
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Correlation between researchers’ centrality and h-index: a case study
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Analysis of the co-authorship sub-networks of Italian academic researchers
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(Unintended) Consequences of export restrictions on medical goods during the Covid-19 pandemic
The outbreak of the Covid-19 pandemic led several governments to impose restrictions on the export of medical supplies. Despite being …
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Machine learning dismantling and early-warning signals of disintegration in complex systems
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies …
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wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide …
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arXiv
Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks
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mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms …
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Food Recommendation in a Worksite Canteen.
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Analysis of the Co-authorship Sub-networks of Italian Academic Researchers
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A Network-Based Analysis of a Worksite Canteen Dataset
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Group Cohesion Assessment in Networks
Networks measurement is essential to catch and quantify their features, behaviour and/or emerging phenomena. The goal of cohesiveness …
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DOI
Learning fine-grained search space pruning and heuristics for combinatorial optimization
Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and …
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arXiv
Strategies comparison in link building problem
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A network-based analysis to understand food-habits of a multi-company canteen's customers
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A PageRank Inspired Approach to Measure Network Cohesiveness
Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the …
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Strategies Comparison in Link Building Problem
Choosing an effective yet efficient solution to the link building problem means to select which nodes in a network should point a …
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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 …
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Long distance connections for ranking and robustness enhancement in networks
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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 …
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arXiv
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 …
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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, …
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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 …
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