MZ Matteo Zignani

Network science · Graph machine learning · Data mining

Matteo Zignani

Associate Professor of Computer Science

I study the structure and evolution of complex networks, with a focus on methods that combine network science, data mining, and machine learning on graphs.

Selected publications University profile
19 h-index Google Scholar
1,408 citations Google Scholar
2024– associate professor University of Milan

Scholar metrics: CV snapshot, 27 March 2026

Research

Understanding networks in motion.

My work addresses temporal, multilayer, and heterogeneous graphs in evolving socio-technical systems. Current research spans temporal graph learning, graph neural networks, graph evolution rules, and network motifs, with applications to online communities, Web3 ecosystems, knowledge graphs, mobility data, and creative uses of generative AI.

01

Temporal graph learning

Representation learning and forecasting methods for dynamic networks, including link prediction and explainability.

02

Web3 socio-economic systems

Graph-based analysis of blockchain social platforms, stablecoin ecosystems, user migration, and anomalous behaviour.

03

Heterogeneous knowledge graphs

Temporal and biomedical knowledge graphs for forecasting, representation learning, and data-driven discovery.

04

Human mobility and social behaviour

Large-scale analysis of mobility, proximity, financial, and platform data through temporal and multilayer networks.

Selected publications

Recent work.

View all on Google Scholar ↗

Projects

Applied research across domains.

Current and recent projects connect methodological work on graph learning with Web3, cybersecurity, biomedical data, and the digital humanities.

2023–2026

AWESOME

Analysis framework for Web3 Social Media

Scientific responsibility for network modelling and mining of blockchain-based social media through temporal heterogeneous graphs.

2023–2026

SERICS

Security and Rights in CyberSpace

Research on AI and machine learning methods for data protection, selective data sharing, and governance-oriented scenarios.

2022–2026

CN3

National Center for Gene Therapy and Drugs based on RNA Technology

Knowledge graphs and graph representation learning for RNA drug analysis and target prioritisation.

2024–2025

GPTheatre

Generative AI for Humanities

Design and implementation of LLM-based interaction for a multimodal theatre experiment, including iterative prompt improvement.

Teaching

Courses and supervision.

I teach graph machine learning, network science, social media analysis, and programming across undergraduate, graduate, doctoral, and postgraduate programmes.

  1. 2025–

    Machine Learning on Graphs

    MSc in Computer Science

  2. 2023–

    Network Science

    MSc in Data Science for Economics

  3. 2020–

    Social Media Mining

    BSc in Digital Communication

  4. 2019–

    Foundations of Digital Social Media

    BSc in Digital Communication

  5. 2019–

    Python Programming Lab

    Master in Digital Humanities

Academic service

Community and recognition.

Service

  • Guest Editor for Applied Network Science, Complexity, and Social Network Analysis and Mining.
  • Publication Chair for Complex Networks conferences from 2016 to 2026.
  • Program Chair for AOC 2016 and workshops at CCS 2020, CCS 2021, and ECML/PKDD 2023.
  • Reviewer and programme committee member for journals and conferences in network science, data mining, and machine learning.

Selected awards

  • Best Runner-Up Paper Award, ACM GoodIT 2021
  • Future Internet Travel Award, ICWSM 2019
  • Best Poster Award, Complex Networks 2017
  • Elsevier Outstanding Reviewer, Computer Communications 2014

Career

Academic path.

  1. 2024–present

    Associate Professor

    University of Milan

  2. 2021–2024

    Assistant Professor (RTD-B)

    University of Milan

  3. 2019–2021

    Assistant Professor (RTD-A)

    University of Milan

  4. 2014–2018

    Postdoctoral Research Fellow

    University of Milan

  5. 2011–2014

    PhD in Computer Science

    University of Milan

Contact

Let’s discuss networks, data, and collaboration.

Department of Computer Science “Giovanni degli Antoni”
Via Celoria 18, Milan