Antonio Cruciani

Postdoctoral Researcher in Computer Science

Aalto University

Distributed Algorithms group

antonio.cruciani AT aalto.fi

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), supervised by Francesco Pasquale and co-supervised by Pierluigi Crescenzi.

Research Interests

• Distributed Computing with emphasis on Dynamic Networks with Churn, Node-Capacitated Clique, and CONGEST.
• Approximation algorithms with emphasis on randomized algorithms for graph mining problems in static and temporal graphs.

News

• February 2026: I will be visiting Leonardo Pellegrina at the University of Padua!
• January 2026: One paper accepted at SIROCCO 2026, see you all in Durham!
• 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!

Publications

Thesis

Conferences

Workshops and Posters

Preprints

Talks

Projects

Ongoing

• Computation in Networks: Robust Foundations [Project description]
• Dynamic Networks with Churn
  With John Augustine, Thorsten Götte
• Efficient graph mining algorithms
  With Leonardo Pellegrina
• Computations on anonymus congested graphs
  With John Augustine, Shay Kutten, David Peleg, Manish Kumar
• Computations on anonymous P2P Networks
  With Francesco Pasquale

Past

• Bioinspired distributed protocols on dynamic networks
  With John Augustine, Shay Kutten, Yuval Emek
• Fast Estimation of geometric centralities on very large graphs
  With Giambattista Amati, Paola Vocca, Simone Angelini, Daniele Pasquini
• Dynamic Networks with Churn
  With John Augustine, Iqra Altaf Gillani
• Models, Generator and Graph Mining Algorithms for Time Dependent Graphs
  With Francesco Pasquale, Pierluigi Crescenzi
• Estimation of distance-based metrics for very large graphs with MinHash Signatures
  With Giambattista Amati, Paola Vocca, Simone Angelini, Daniele Pasquini [Code]
• Simple dynamic random graph models
  With Francesco Pasquale [Code]
• Topic modeling by community detection algorithms
  With Giambattista Amati, Paola Vocca, Simone Angelini, Daniele Pasquini

CV

Full CV

Code

• PercIS: Fast Percolation Centrality Approximation with Importance Sampling. PercIS is a sampling-based approximation algorithm based on importance sampling to approximate the percolation centrality of all nodes of a graph. [Code]
• FEPiC: Fast Estimation of Percolation Centrality. FEPiC is a progressive sampling algorithm for approximating doubly normalized percolation centrality, using Monte Carlo Empirical Rademacher Averages and variance-aware bounds. The implementation includes progressive, fixed-sample, and exact variants. [Code][arXiv]
• MANTRA: Temporal Betweenness Centrality Approximation through Sampling. MANTRA is a progressive sampling algorithm for temporal betweenness centrality in temporal graphs. The library also includes algorithms for temporal edge centrality, temporal diameter, average path length, and connectivity rate. [Code][arXiv]
• PROPAGATE: a seed propagation framework to compute Distance-based metrics on Very Large Graphs. PROPAGATE is a sampling-based algorithm to approximate distance-based metrics such as diameter, effective diameter, average distance, reachable pairs, connectivity rate, and geometric centralities. [Code][arXiv]
• TSBProxy: Temporal Shortest Betweenness Proxy. TSBProxy is a suite of proxies for temporal shortest betweenness centrality, including Julia implementations of shortest and prefix foremost betweenness, ONBRA, and a novel temporal degree proxy. [Code]
• DREG: Dynamic Random Expander Generator. DREG is a Python implementation of a generator for almost regular dynamic random graphs that are expanders at each time step with high probability. [Code]

Teaching

• Course: CS-E4565 - Combinatorics of Computation D. (2025)
  Teaching Assistant, covering the first two weeks: Introduction to probability, the probabilistic method and derandomization.
  Professor: Jara Uitto
  Institute: Aalto University [First lesson][Second lesson][Third lesson][Fourth lesson]
• Theoretical Computer science - Exercise sessions. (Academic Year 2018-2019 (Italian))
  Institute: University of Rome "Tor Vergata" [Exercises]
• Computer Programming - Exercise sessions. (Academic Year 2017-2018 (Italian))
  Institute: University of Rome "Tor Vergata"

Contacts

• Primary email: antonio.cruciani AT aalto.fi
• Secondary email: antonio.cruciani AT gssi.it
• PGP key: antonio_cruciani.asc
• Address: Computer Science
  Aalto University
  Tietotekniikantalo, Konemiehentie 2, room number B313
  Espoo
  Finland
• dblp: dblp
• GitHub: GitHub
• LinkedIn: LinkedIn