Author Archives: misz

New NERDS paper on network analysis of Italian music

A new NERDS authored paper is out in Applied Network Science: Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music, by M. Coscia


We use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.

Claudia Acciai has joined NERDS

We are chuffed to welcome Claudia Acciai to our research group!

Claudia joins us as Postdoc, coming from the Department of Sociology at University of Copenhagen (KU), where she was working on quantifying institutional and country-related Matthew effects in science.

Her work lies at the intersection of comparative public policy, innovation studies and science of science. In her research she combines computational and experimental methods with qualitative content analysis techniques.

At NERDS she joins via the Villum Synergy project Quantifying the Prevalence and Diffusion of Generative AI in Science, supervised by Roberta Sinatra, collaborating closely also with the project’s second PI, Mathias Wullum Nielsen.

Three new NERDS papers with our master students: Failing our youngest, superblockify, women on wikipedia

We have 3 new papers that came out over the summer so far, on diverse, very interesting topics. The first authors in all 3 of these papers were our master students – showing how impactful good master projects can be:

  1. Failing Our Youngest: On the Biases, Pitfalls, and Risks in a Decision Support Algorithm Used for Child Protection, by T.M. Hansen, R. Sinatra, and V. Sekara, published at FAccT’24
    Through a freedom of information request, we accessed a new algorithm of Danish child protection services to aid caseworkers in identifying children at heightened risk of maltreatment, named Decision Support, and conduct an audit. We find that the algorithm has significant methodological flaws, suffers from information leakage, relies on inappropriate proxy values for maltreatment assessment, generates inconsistent risk scores, and exhibits age-based discrimination. Given these serious issues, we strongly advise against the use of this kind of algorithms in local government, municipal, and child protection settings, and we call for rigorous evaluation of such tools before implementation and for continual monitoring post-deployment by listing a series of specific recommendations.

    See also our accompanying policy paper published earlier.
  2. superblockify: A Python Package for Automated Generation, Visualization, and Analysis of Potential Superblocks in Cities, by C.M. Büth, A. Vybornova, and M. Szell, published in The Journal of Open Source Software (JOSS)
    superblockify is a Python package designed to assist in planning future Superblock implementations by partitioning an urban street network into Superblock-like neighborhoods and providing tools for visualizing and analyzing these partition results. A Superblock is a set of adjacent urban blocks where vehicular through traffic is prevented or pacified, giving priority to people walking and cycling. The potential Superblock blueprints
    and descriptive statistics generated by superblockify can be used by urban planners as a first step in a data-driven planning pipeline for future urban transformations, or by urban data scientists as an efficient computational method to evaluate potential Superblock partitions.


    The software is available at: superblockify.city
  3. Traces of Unequal Entry Requirement for Illustrious People on Wikipedia Based on their Gender, by L. Krivaa and M. Coscia, published in Advances in Complex Systems
    In this paper, we study issues of fair gender representations for people in history noted by multiple language editions of Wikipedia: are women underrepresented on Wikipedia? We do so via a combination of natural language processing and network science. Our results indicate that there is indeed a higher bar for women to have their own biographical page on Wikipedia: women are only included when they have more significant connections than men to the rest of the network. There are visible effects of the initiatives Wikipedia is taking to fix this issue, showing that the gap is narrowing, which validates our interpretation of the data.

Jonas L. Juul has joined NERDS

We are thrilled to welcome Jonas L. Juul to our research group!

Jonas joins us as Assistant Professor, after an illustrious past in network science, having worked -among others- with Mason Porter, Steven Strogatz, Jon Kleinberg, and Sune Lehmann *hashtag namedrop*. He uses statistical methods, mathematical modeling and computer simulations to study social networks, spreading processes and human behavior. Recently, he has been particularly interested in how content spreads between online users, and how to mitigate the spread of diseases in human populations. He approaches these questions both empirically — using methods from modern data science — and theoretically with methods from physics and mathematical modeling.

Jonas had past professional roles at Technical University of Denmark and Cornell University, and he was also part of the Statens Serum Institut’s expert group on mathematical modeling of COVID-19 during the reopening of Denmark in the spring and summer of 2020. Check out Jonas’ cool Webpage to find more information about him.

New NERDS paper out on Machine Learning in Humanitarian Work

We published a new paper:

THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT, by V. Sekara, M. Karsai, E. Moro, D. Kim, E. Delamonica, M. Cebrian, M. Luengo-Oroz, R. Moreno Jimenez, M. Garcia-Herranz, published in Advances in Complex Systems

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

New NERDS paper out on success in tennis

We published a new multi-NERDS paper, concluding a successful previous internship of Chiara Zappalà!

Early career wins and tournament prestige characterize tennis players’ trajectories, by C. Zappalà, S. Sousa, T. Cunha, A. Pluchino, A. Rapisarda and R. Sinatra, published in EPJ Data Science


We study the unfolding of tennis players’ careers to understand the role of early career stages and the impact of specific tournaments on players’ trajectories. We employ a comprehensive approach combining network science and analysis of the Association of Tennis Professionals (ATP) tournament data and introduce a novel method to quantify tournament prestige based on the eigenvector centrality of the co-attendance network of tournaments. Focusing on the interplay between participation in central tournaments and players’ performance, we find that the level of the tournament where players achieve their first win is associated with becoming a top player. This work sheds light on the critical role of the initial stages in the progression of players’ careers, offering valuable insights into the dynamics of success in tennis.

5 years of NERDS!

NERDS was founded 5 years ago by three young assistant professors and one associate professor, to be a reference point at ITU for the research on network and data science applications to social systems. At 5 years old, we have learned to write our name (we have a logo), to follow rules, and to use a fork and knife for eating. And hoo boy, did we use that fork last friday when celebrating our anniversary with an original NERDS cake!

We also got new group photos taken (by Sebastian Mateos Nicolajsen – thx!), see below, because we have grown to 20+ members over the years! For all bean counting aficionados, we also won over 5M EUR of research funding and published 84 papers so far.

Further highlights, shown in the timeline above. We:

Looking back to our goals 5 years ago, we have all reason to be proud to have 1) built up a flourishing network of Denmark-based network/data science research groups, connecting ITU, KU, DTU, and others, 2) successfully impressed several funding agencies and public stakeholders to engage with us solving societal problems with our research. We will continue along this road, developing further our group in a safe and fun environment.

In our near future, we look forward to welcoming several new group members in the fall, including one assistant professor and several PhDs/Postdocs.

Live long and prosper 🖖
Luca, Luigi, Roberta, Claudia, Anastassia, Jacob, Vedran, Sandro, Anders, Michele, Anders 2, Ane, Toine, Mesut, Luca 2, Michael, Arianna, Clement, Elisabetta, Alessia, Jacopo, Daniele, Nicoló

Mesut Kaya has joined NERDS

We are happy to welcome Mesut Kaya to our research group!

Mesut joins us as an Industrial Postdoc funded by the Innovation Fund. Previously, Mesut was at Aalborg University Copenhagen; before in Delft University and University College Cork. Mesut works on recommender systems in general, at NERDS he will be working with Toine Bogers specifically on recommendation in the HR domain: job recommendation, candidate recommendation, and now the fairness of algorithmic hiring in general.

New NERDS paper out on bicycle network quality in Denmark

We published a new all-NERDS paper, applying our BikeDNA tool to the whole country of Denmark as part of our Cykelpulje project!

How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark, by A. Rahbek Vierø, A. Vybornova, and M. Szell, published in Geographical Analysis


We compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data.

Explore also the interactive map: https://anerv.github.io/bikedna_webmap/

GrowBike.Net covered in Austrian media

Our project GrowBike.Net was just covered in Austrian media, both in an online news report and on Austrian TV station ORF 2, in the news report Wien heute. If you understand German, watch our 15 seconds of fame here: https://tvthek.orf.at/profile/Wien-heute/70018/Wien-heute-vom-27-03-2024/14219951/Radinfrastruktur-ausbaufaehig/15608017

The written report Radwege sollen möglichst direkt verlaufen: https://wien.orf.at/stories/3249925/

GrowBike.Net is an interactive platform resulting from an ITU master project, visualizing the results of our 2022 paper Growing urban bicycle networks: https://www.nature.com/articles/s41598-022-10783-y. The idea is to simulate the creation of a cohesive bicycle network inspired by the Dutch CROW design manual for bicycle traffic. Studying these synthetic networks informs us about the geometric limitations of urban bicycle network growth and can lead to better designed bicycle infrastructure in cities. In the case of the Austrian news report, the key metric of directness was highlighted: Bicycle networks should allow for direct paths without substantial detours.