I am a postdoctoral researcher in Computer Science at Aalto University working in Jukka Suomela's Distributed Algorithms group.
I earned my PhD in Computer Science from Gran Sasso Science Institute (Italy) and I was supervised by Francesco Pasquale and co-supervised by Pierluigi Crescenzi
My research is focused on
Distributed Computing with emphasis on three models:
Dynamic Network with Churn, Node Capacitated Clique and CONGEST.
Approximation Algorithms with emphasis on randomized algorithms. In particular for graph mining problems in static and temporal graphs.
News
August 2025: One paper accepted at ICDM 2025, see you all in DC!
August 2025: Three papers accepted at DISC 2025, see you all in Berlin!
March 2025: I successfully defended my PhD, and I am starting a new position as a postdoctoral researcher at Aalto University.
August 2024: I joined again John Augustine at IIT Madras to work on Dynamic Networks with Churns.
Fast Percolation Centrality Approximation with Importance Sampling. (ICDM 2025 - To appear) [arXiv]. Antonio Cruciani and Leonardo Pellegrina.
Maintaining Distributed Data Structures in Dynamic Peer-to-Peer Networks. (DISC 2025 - To appear) [arXiv]. John Augustine, Antonio Cruciani, Iqra Altaf Gillani.
Maintaining a Bounded Degree Expander in Dynamic Peer-to-Peer Networks. (DISC 2025 - To appear) [arXiv]. Antonio Cruciani.
New Limits on Distributed Quantum Advantage: Dequantizing Linear Programs. (DISC 2025 - To appear) [arXiv]. Alkida Balliu, Corinna Coupette, Antonio Cruciani, Francesco d'Amore, Massimo Equi, Henrik Lievonen, Augusto Modanese, Dennis Olivetti, Jukka Suomela.
Maintaining Distributed Data Structures in Dynamic Peer-to-Peer Networks. IIT Madras - Aalto University (online) 2024 Slides
On the Temporal Betweenness Centrality. IIT Madras 2024 Slides
MANTRA: Temporal Betweenness Centrality Approximation through Sampling. ECML-PKDD 2024Slides
Computing Distance-based metrics on Very Large Graphs. University of Padua 2024 Slides
PROPAGATE: a seed propagation framework to compute Distance-based metrics on Very Large Graphs.ECML-PKDD 2023Slides
Proxying Betweenness Centrality Rankings in Temporal Networks.SEA 2023Slides
Dynamic graph models inspired by the Bitcoin network-formation process.ICDCN 2023Slides
Dynamic graph models for the Bitcoin P2P network: simulation analysis for expansion and flooding time. SSS 2022Slides
Code
PercIS: Fast Percolation Centrality Approximation with Importance Sampling. [Code]
PercIS is a sampling based approximation algorithm based on Importance Sampling to approximate the percolation centrality of all the nodes of a graph. Percolation centrality is a generalization of betweenness centrality to attributed graphs, and is a useful measure to quantify the importance of the vertices in a contagious process or to diffuse information. However, it is impractical to compute it exactly on modern-sized networks.
FEPiC: Fast Estimation of Percolation Centrality. [Code]
FEPiC is a progressive sampling based algorithm to approximate the (doubly normalized) percolation centrality of all nodes in a static graph. FEPiC uses Monte Carlo Empirical Rademacher Averages and variance aware bounds for the sample complexity to provide a high quality approximation of such a centrality measure. FEPiC extends the novel algorithm for the Betweenness Centrality by [Pellegrina and Vandin] to the Percolation Centrality. FEPiC is the best choice in terms of sample size, running time and quality of the approximation. The Julia library includes efficient (parallel) implementation of: (i) FEPiC (see [arXiv] for the original paper); (ii) the progressive sampling algorithm by [Lima et al.]; (iii) the fixed sample size algorithm by [Lima et al.]; and, an efficient version of the exact algorithm for the percolation centrality. All the algorithms use multithreading and outperform the already existing NetworkX implementations.
MANTRA: Temporal Betweenness Centrality Approximation through Sampling. [Code]
MANTRA is a progressive sampling based algorithm to approximate the temporal betweenness centrality of all nodes in a temporal graph. MANTRA uses Monte Carlo Empirical Rademacher Averages and variance aware results on sample complexity to provide a high quality approximation of the Shortest (Foremost) and Prefix Foremost Temporal Betweenness centralities [Buß et al.]. MANTRA is the best choice in terms of sample size, running time, and required space (linear in the size of the temporal graph) to provide high quality approximations. The library includes algorithms for computing temporal edge centrality, temporal diameter, average path length, and connectivity rate. See [arXiv].
PROPAGATE: a seed propagation framework to compute Distance-based metrics on Very Large Graphs. [Code]
PROPAGATE is a sampling based algorithm to approximate all the distance-based metrics (diameter, effective diameter, average distance, number of reachable pairs, connectivity rate, and geometric centralities). The Java library includes two efficient approximation algorithms (see [arXiv]). PROPAGATE is the best choice in terms of running time and required space.
TSBProxy is a suite of proxies for Temporal Shortest Betweenness Centrality. It includes Julia implementations of the Shortest and Prefix Foremost Betweenness algorithms [Buß et al.], ONBRA [Santoro et al.], and a novel temporal degree proxy.
