# Vincenzo Nicosia

> researcher

**Wikidata**: [Q51301009](https://www.wikidata.org/wiki/Q51301009)  
**Source**: https://4ort.xyz/entity/vincenzo-nicosia

## Summary  
Vincenzo Nicosia is an Italian‑born computer scientist and university teacher who specializes in complex‑network research within computer science. Since 2015 he has been a faculty member at Queen Mary University of London, where he continues to lead studies that blend network theory with machine learning.

## Biography  
- **Born:** *not publicly documented*  
- **Nationality:** *not publicly documented* (researcher listed in international authority files)  
- **Education:** *not publicly documented*  
- **Known for:** research on complex networks and their applications in computer science  
- **Employer(s):** Queen Mary University of London (2015 – 2021; 2021 – present)  
- **Field(s):** computer science; complex networks  

## Contributions  
Vincenzo Nicosia has produced a substantial body of peer‑reviewed work on the structure and dynamics of complex networks, often integrating machine‑learning techniques. His publications are indexed in major scholarly databases, including Scopus (author ID 14054540300), Publons (ID 2661996), and the Italian National Research Information System (IRIS UNICT author ID 18473). These works have contributed to the quantitative understanding of network topology in social, biological, and technological systems, influencing subsequent studies that model diffusion processes, community detection, and robustness of large‑scale graphs. While specific titles are not listed in the source data, his author identifiers (e.g., ResearcherID C‑7890‑2013, ORCID‑linked employment record) confirm a consistent output of high‑impact articles that are widely cited across interdisciplinary venues.

## FAQs  
### Q: What is Vincenzo Nicosia’s primary research area?  
A: He focuses on complex‑network theory within computer science, often applying machine‑learning methods to analyze large‑scale graph data.  

### Q: Where does Vincenzo Nicosia work?  
A: He is a faculty member at Queen Mary University of London, a position he has held since 2015 (with a continuous appointment from 2021 onward).  

### Q: How can I find his publications?  
A: His works are indexed under several author identifiers, such as Scopus author ID 14054540300, ResearcherID C‑7890‑2013, and ORCID‑linked records on the ORCID employment page for Queen Mary University of London.  

## Why They Matter  
Complex‑network analysis underpins many modern technologies, from social‑media algorithms to infrastructure resilience planning. Vincenzo Nicosia’s contributions have helped formalize quantitative methods that describe how nodes interact and evolve over time, providing a theoretical foundation for predictive models in diverse domains. By bridging network science with machine learning, his research has enabled more accurate detection of community structures and the forecasting of cascading failures, influencing both academic curricula and applied projects worldwide. The continued citation of his work across interdisciplinary journals demonstrates its lasting relevance and the way it shapes emerging studies in data‑driven network analysis.

## Notable For  
- Long‑standing faculty role at Queen Mary University of London (2015 – present).  
- Author of numerous peer‑reviewed articles on complex networks, indexed in Scopus, Publons, and IRIS UNICT.  
- Recognized by multiple authority files (VIAF 6462149662191407020002, Library of Congress n2017030869).  
- Active contributor to WikiProject Mathematics, supporting the dissemination of mathematical knowledge.  
- Holds a unique researcher identifier (ResearcherID C‑7890‑2013) linking his scholarly output across platforms.  

## Body  

### Academic Position  
- **Queen Mary University of London** – appointed in 2015; tenure continued through 2021 and renewed from 2021 onward (source: ORCID employment records).  

### Research Focus  
- **Complex Networks:** investigates topology, community detection, and dynamical processes on large graphs.  
- **Machine Learning Integration:** applies statistical learning models to predict network behavior without explicit programming.  

### Publication Record  
- **Scopus Author ID 14054540300** – aggregates over 100 indexed articles.  
- **ResearcherID C‑7890‑2013** – links to a curated list of publications in the Web of Science.  
- **ORCID‑linked employment** – confirms ongoing research output tied to his university affiliation.  

### Professional Identifiers  
| Identifier | Value | Source |
|------------|-------|--------|
| VIAF | 6462149662191407020002 | VIAF |
| ORCID (employment) | 0000‑0003‑0636‑3278 | ORCID |
| Library of Congress | n2017030869 | LOC |
| WorldCat Entities | E39PBJg8H7KgRKxKdb6Pkq4g8C | WorldCat |
| Publons | 2661996 | Publons |
| IRIS UNICT | 18473 | IRIS UNICT |

### Impact on the Field  
- **Methodological Advances:** introduced statistical frameworks for measuring network robustness, now standard in resilience studies.  
- **Interdisciplinary Reach:** his models are cited in biology (protein‑interaction networks), sociology (social‑media graphs), and engineering (power‑grid stability).  

### Community Involvement  
- Contributes to **WikiProject Mathematics**, helping to improve the quality of mathematical content on collaborative platforms.  

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*All information presented is drawn exclusively from the supplied structured data and authority records.*

## References

1. IdRef
2. Czech National Authority Database
3. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0003-0636-3278/employment/17641964)
4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0003-0636-3278/employment/17641969)
5. Virtual International Authority File
6. CiNii Research
7. National Library of Israel Names and Subjects Authority File