Francesco D'AMore
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Francesco D'AMore has a total of 30 co-authors. Here are the top co-authors by number of shared publications:
- Emanuele Natale
CNRS, COATI, I3S, Université Côte d'Azur, Sophia Antipolis, France
4 shared publications - Jukka Suomela
Aalto University, Espoo, Finland
4 shared publications - Alkida Balliu
Gran Sasso Science Institute, L'Aquila, Italy
3 shared publications - Andrea Clementi
Department of Enterprise Engineering, University of Rome 'Tor Vergata', Rome, Italy
3 shared publications - Augusto Modanese
Aalto University, Espoo, Finland
3 shared publications - Dennis Olivetti
Gran Sasso Science Institute, L'Aquila, Italy
3 shared publications
Latest Publications
- Open Access
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 154-165
Distributed Algorithms for Potential Problems
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 154-165
Open AccessDistributed Algorithms for Potential Problems
- Open Access
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 437-440
Brief Announcement: DéjàVu: A Minimalistic Mechanism for Distributed Plurality Consensus
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 437-440
Open AccessBrief Announcement: DéjàVu: A Minimalistic Mechanism for Distributed Plurality Consensus
- Open Access
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 301-310
New Hardness Results for the LOCAL Model via a Simple Self-Reduction
- 6 July 2026
- Published by Association for Computing Machinery (ACM)
- p. 301-310
Open AccessNew Hardness Results for the LOCAL Model via a Simple Self-Reduction
- 7 April 2026
- Published by Agence Bibliographique de l'Enseignement Supérieur
On the collective behaviors of bio-inspired distributed systems
Sur les comportements collectifs de systèmes distribués bio-inspirés: Récemment, la communauté algorithmique a manifesté un intérêt croissant pour l'utilisation de ses outils théoriques à la compréhension des systèmes complexes, notamment biologiques, tels que les colonies d'insectes, les volées d'oiseaux et les réseaux de neurones. Nous contribuons à l'étude de ces systèmes dans trois directions différentes. Premièrement, nous analysons des dynamiques computationnelles pour les tâches de coordination stochastique dans les systèmes multi-agents. En particulier, nous nous focalisons sur le problème du consensus dans des environnements où la communication est bruyante : nous analysons deux dynamiques d'opinion, les dynamiques Undecided-State et 3-Majority, et nous prouvons qu'elles présentent une transition de phase à des seuils de bruit différents. En dessous du seuil, ces dynamiques atteignent rapidement une phase métastable de quasi-consensus ; au-dessus, aucune forme de consensus n'est possible. Deuxièmement, nous étudions les Lévy walks, des marches aléatoires qui modélisent des schémas de mouvement trouvés dans la nature, dont la distribution de la longueur de pas suit une loi de puissance. Nous analysons leur temps d'arrêt (hitting time) parallèle et les utilisons pour concevoir un algorithme optimal pour l'ANTS problem, un problème de recherche distribuée sur Z^2 qui capture certains aspects de la théorie du butinage. Troisièmement, nous considérons l'Assembly Calculus, un modèle distribué du cerveau récemment proposé, qui consiste en des neurones et des synapses stylisés, et nous testons expérimentalement ses capacités, largement inexplorées, en mettant en œuvre des heuristiques connues pour la tâche de planification du monde des blocs. Nous montrons empiriquement que des programmes grands et complexes dans ce modèle s'exécutent correctement et de manière fiable.
- 7 April 2026
- Published by Agence Bibliographique de l'Enseignement Supérieur
On the collective behaviors of bio-inspired distributed systems
Sur les comportements collectifs de systèmes distribués bio-inspirés: Récemment, la communauté algorithmique a manifesté un intérêt croissant pour l'utilisation de ses outils théoriques à la compréhension des systèmes complexes, notamment biologiques, tels que les colonies d'insectes, les volées d'oiseaux et les réseaux de neurones. Nous contribuons à l'étude de ces systèmes dans trois directions différentes. Premièrement, nous analysons des dynamiques computationnelles pour les tâches de coordination stochastique dans les systèmes multi-agents. En particulier, nous nous focalisons sur le problème du consensus dans des environnements où la communication est bruyante : nous analysons deux dynamiques d'opinion, les dynamiques Undecided-State et 3-Majority, et nous prouvons qu'elles présentent une transition de phase à des seuils de bruit différents. En dessous du seuil, ces dynamiques atteignent rapidement une phase métastable de quasi-consensus ; au-dessus, aucune forme de consensus n'est possible. Deuxièmement, nous étudions les Lévy walks, des marches aléatoires qui modélisent des schémas de mouvement trouvés dans la nature, dont la distribution de la longueur de pas suit une loi de puissance. Nous analysons leur temps d'arrêt (hitting time) parallèle et les utilisons pour concevoir un algorithme optimal pour l'ANTS problem, un problème de recherche distribuée sur Z^2 qui capture certains aspects de la théorie du butinage. Troisièmement, nous considérons l'Assembly Calculus, un modèle distribué du cerveau récemment proposé, qui consiste en des neurones et des synapses stylisés, et nous testons expérimentalement ses capacités, largement inexplorées, en mettant en œuvre des heuristiques connues pour la tâche de planification du monde des blocs. Nous montrons empiriquement que des programmes grands et complexes dans ce modèle s'exécutent correctement et de manière fiable.
- Open Access
- 15 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 1295-1306
Online Locality Meets Distributed Quantum Computing
- 15 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 1295-1306
Open AccessOnline Locality Meets Distributed Quantum Computing
- Open Access
- 15 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 451-462
Distributed Quantum Advantage for Local Problems
- 15 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 451-462
Open AccessDistributed Quantum Advantage for Local Problems
- Open Access
- 11 December 2024
- Published by Elsevier in Theoretical Computer Science
- Vol. 1028
Phase transition of the 3-majority opinion dynamics with noisy interactions
- 11 December 2024
- Published by Elsevier in Theoretical Computer Science
- Vol. 1028
Open AccessPhase transition of the 3-majority opinion dynamics with noisy interactions
- Open Access
- 10 June 2024
- Published by Association for Computing Machinery (ACM)
- p. 1901-1910
No Distributed Quantum Advantage for Approximate Graph Coloring
We give an almost complete characterization of the hardness of c-coloring χ-chromatic graphs with distributed algorithms, for a wide range of models of distributed computing. In particular, we show that these problems do not admit any distributed quantum advantage. To do that: We give a new distributed algorithm that finds a c-coloring in χ-chromatic graphs in Õ(n1/α) rounds, with α = ⌊c−1/χ − 1⌋. We prove that any distributed algorithm for this problem requires Ω(n1/α) rounds. Our upper bound holds in the classical, deterministic LOCAL model, while the near-matching lower bound holds in the non-signaling model. This model, introduced by Arfaoui and Fraigniaud in 2014, captures all models of distributed graph algorithms that obey physical causality; this includes not only classical deterministic LOCAL and randomized LOCAL but also quantum-LOCAL, even with a pre-shared quantum state. We also show that similar arguments can be used to prove that, e.g., 3-coloring 2-dimensional grids or c-coloring trees remain hard problems even for the non-signaling model, and in particular do not admit any quantum advantage. Our lower-bound arguments are purely graph-theoretic at heart; no background on quantum information theory is needed to establish the proofs.
- 10 June 2024
- Published by Association for Computing Machinery (ACM)
- p. 1901-1910
Open AccessNo Distributed Quantum Advantage for Approximate Graph Coloring
We give an almost complete characterization of the hardness of c-coloring χ-chromatic graphs with distributed algorithms, for a wide range of models of distributed computing. In particular, we show that these problems do not admit any distributed quantum advantage. To do that: We give a new distributed algorithm that finds a c-coloring in χ-chromatic graphs in Õ(n1/α) rounds, with α = ⌊c−1/χ − 1⌋. We prove that any distributed algorithm for this problem requires Ω(n1/α) rounds. Our upper bound holds in the classical, deterministic LOCAL model, while the near-matching lower bound holds in the non-signaling model. This model, introduced by Arfaoui and Fraigniaud in 2014, captures all models of distributed graph algorithms that obey physical causality; this includes not only classical deterministic LOCAL and randomized LOCAL but also quantum-LOCAL, even with a pre-shared quantum state. We also show that similar arguments can be used to prove that, e.g., 3-coloring 2-dimensional grids or c-coloring trees remain hard problems even for the non-signaling model, and in particular do not admit any quantum advantage. Our lower-bound arguments are purely graph-theoretic at heart; no background on quantum information theory is needed to establish the proofs.
- Open AccessSearch via Parallel Lévy Walks on $\mathbb{Z}^2$Motivated by the L\'evy foraging hypothesis -- the premise that various animal species have adapted to follow L\'evy walks to optimize their search efficiency -- we study the parallel hitting time of L\'evy walks on the infinite two-dimensional grid. We consider $k$ independent discrete-time L\'evy walks, with the same exponent $\alpha \in(1,\infty)$, that start from the same node, and analyze the number of steps until the first walk visits a given target at distance $\ell$. We show that for any choice of $k$ and $\ell$ from a large range, there is a unique optimal exponent $\alpha_{k,\ell} \in (2,3)$, for which the hitting time is $\tilde O(\ell^2/k)$ w.h.p., while modifying the exponent by an $\epsilon$ term increases the hitting time by a polynomial factor, or the walks fail to hit the target almost surely. Based on that, we propose a surprisingly simple and effective parallel search strategy, for the setting where $k$ and $\ell$ are unknown: the exponent of each L\'evy walk is just chosen independently and uniformly at random from the interval $(2,3)$. This strategy achieves optimal search time (modulo polylogarithmic factors) among all possible algorithms (even centralized ones that know $k$). Our results should be contrasted with a line of previous work showing that the exponent $\alpha = 2$ is optimal for various search problems. In our setting of $k$ parallel walks, we show that the optimal exponent depends on $k$ and $\ell$, and that randomizing the choice of the exponents works simultaneously for all $k$ and $\ell$.Open AccessSearch via Parallel Lévy Walks on $\mathbb{Z}^2$Motivated by the L\'evy foraging hypothesis -- the premise that various animal species have adapted to follow L\'evy walks to optimize their search efficiency -- we study the parallel hitting time of L\'evy walks on the infinite two-dimensional grid. We consider $k$ independent discrete-time L\'evy walks, with the same exponent $\alpha \in(1,\infty)$, that start from the same node, and analyze the number of steps until the first walk visits a given target at distance $\ell$. We show that for any choice of $k$ and $\ell$ from a large range, there is a unique optimal exponent $\alpha_{k,\ell} \in (2,3)$, for which the hitting time is $\tilde O(\ell^2/k)$ w.h.p., while modifying the exponent by an $\epsilon$ term increases the hitting time by a polynomial factor, or the walks fail to hit the target almost surely. Based on that, we propose a surprisingly simple and effective parallel search strategy, for the setting where $k$ and $\ell$ are unknown: the exponent of each L\'evy walk is just chosen independently and uniformly at random from the interval $(2,3)$. This strategy achieves optimal search time (modulo polylogarithmic factors) among all possible algorithms (even centralized ones that know $k$). Our results should be contrasted with a line of previous work showing that the exponent $\alpha = 2$ is optimal for various search problems. In our setting of $k$ parallel walks, we show that the optimal exponent depends on $k$ and $\ell$, and that randomizing the choice of the exponents works simultaneously for all $k$ and $\ell$.
- 17 November 2022
- Published by Springer Nature in Swarm Intelligence
- Vol. 16 (4) , 261-304
Phase transition of a nonlinear opinion dynamics with noisy interactions
- 17 November 2022
- Published by Springer Nature in Swarm Intelligence
- Vol. 16 (4) , 261-304
Phase transition of a nonlinear opinion dynamics with noisy interactions