We have a new publication out on a piece of software we wrote:

BikeNodePlanner: A data-driven decision support tool for bicycle node network planning, by A. Vybornova, A.R. Vierø, K.K. Hansen, and M. Szell, published in Environment and Planning B

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: A fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

We have three new publications out, on a variety of topics!

1. Extracting Participation in Collective Action from Social Media, by Arianna Pera and Luca Maria Aiello, published in Proceedings of the International AAAI Conference on Web and Social Media.
   
   We present a novel suite of text classifiers designed to identify expressions of participation in collective action from social media posts, in a topic-agnostic fashion. Grounded in the theoretical framework of social movement mobilization, our classification captures participation and categorizes it into four levels: recognizing collective issues, engaging in calls-to-action, expressing intention of action, and reporting active involvement.  We constructed a labeled training dataset of Reddit comments through crowdsourcing, which we used to train BERT classifiers and fine-tune Llama3 models. Our findings show that smaller language models can reliably detect expressions of participation (weighted F1=0.71), and rival larger models in capturing nuanced levels of participation.
2. Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens, by Beatriz Arregui-García, Claudio Ascione, Arianna Pera, Boxuan Wang, Davide Stocco, Colin J. Carlson, Shweta Bansal, Eugenio Valdano, Giulia Pullano published in PLOS Climate.
   
   This study investigates how wildfire-induced changes in human behavior during the U.S. West Coast wildfires of 2020 may affect the spread of airborne diseases. Using a mobility data-driven indoor activity index, we find that the wildfire-induced deterioration of air quality led to a substantial increase in indoor activities, fostering conditions conducive to airborne disease transmission. Specifically, counties in Oregon and Washington experienced an average 10.8% and 14.3% increase in indoor activity, respectively, during the wildfire events, with major cities like Portland and Seattle experiencing increases of 11% and 16%, respectively. We quantify these behavioral changes and integrate them into an SIR epidemic model to characterize the increased indoor activity and disease dynamics. The model predicts the greatest impact on diseases with shorter generation times, such as RSV and influenza.
3. Urban Mobility, by Laura Alessandretti and Michael Szell, book chapter in Compendium of Urban Complexity (Springer)
   
   In this chapter, we discuss urban mobility following a complexity science approach. First, we give an overview of the datasets that enable this approach, such as mobile phone records, location-based social network traces, or GPS trajectories from sensors installed on vehicles. We then review the empirical and theoretical understanding of the properties of human movements, including the distribution of travel distances and times, the entropy of trajectories, and the interplay between exploration and exploitation of locations. Next, we explain generative and predictive models of individual mobility, and their limitations due to intrinsic limits of predictability. Finally, we discuss urban transport from a systemic perspective, including system-wide challenges like ridesharing, multimodality, and sustainable transport.

We have two new publications out!

1. Emergent social conventions and collective bias in LLM populations, by Ariel Flint Ashery, Luca Maria Aiello, and Andrea Baronchelli, published in Science Advances.
   We explore the collective behavior of LLMs starting from social conventions, the fundamental building blocks of coordinated societies. We used the naming game, a well-established framework that has been applied for decades to study conventions in humans. We found that LLM populations can spontaneously develop shared conventions through local interactions. These interactions can produce collective biases, invisible at the individual level, raising important considerations for AI alignment. Small committed minorities can trigger tipping points, steering the entire group toward new conventions—a dynamic well known in human societies
2. The experience of running: Recommending routes using sensory mapping in
   urban environments, by Katrin Hänsel, Luca Maria Aiello, Daniele Quercia, Rossano Schifanella, Krisztian Zsolt Varga, Linus W. Dietz, and Marios Constantinides, published in the International Journal of Human-Computer Studies.
   We set out to build running routes not around distance, but around how people feel: before, during, and after a run. We surveyed 387 runners and found that not everyone wants the same kind of run. Some seek quiet and greenery; others thrive on the buzz of people and traffic. Their preferences often match their personality. Runners who prefer scenic paths (quiet, green, and natural) tended to score higher in neuroticism. Those who preferred urban paths (lively and full of people) were more likely to be extroverted. Then, we built a routing engine. Using millions of geotagged Flickr photos and open London data, we scored streets for beauty, noise, safety, and surface quality. We tested the engine on hundreds of 5-km London loops. Most runners preferred the scenic routes.

We have two new publications out at CHIIR 2025 in Melbourne (Proceedings of the 2025 ACM SIGIR Conference on Human Information Interaction and Retrieval):

1. Exploring the Zero-Shot Known-Item Retrieval Capabilities of LLMs for Casual Leisure Information Needs, by T. Bogers, M. Gäde, M. Hall, M. Koolen, V. Petras, M. Skov, published in CHIIR 2025
   We compared four LLMs on their capability to answer a specific type of complex search task: known-item requests from casual leisure domains. We constructed a test collection by gathering known-item requests for books, games and movies from online forums along with verified answers by the original requester. We prompted four LLMs multiple times with the same prompt and analyzed the results with respect to accuracy and the degree to which answers were fabricated by the LLM. Our results show that LLMs are not particularly effective in fulfilling these complex casual leisure needs, but there are are big differences between LLMs and across domains.
2. From Queries to Candidates: Exploring Search and Source Interaction Behavior of Recruiters, by T. Bogers, M. Kaya, M. Gäde, published in CHIIR 2025
   
   Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters could have a big impact on job seekers, companies and society as a whole. In this paper, we analyze the search formulation and source selection behavior of the recruiters at one of Scandinavia’s largest job portals and recruitment agencies using search logs for close to 18,000 recruitment search tasks. We provide an analysis of the search sessions of recruiters in terms search tactics, query operators, query length, term re-use and filter usage, and break down their behavior both by task type and task complexity. We also relate their short-term tactics to different learning stages in the search process and investigate their influence on search success. We find that identifying and assessing relevant candidates for a job posting is a complex task: recruiters usually submit multiple queries during sessions that can last for hours and that increase in complexity. Recruiters all spend more time per query as their session progresses. We also observed query reformulation strategies that indicate distinct patterns of knowledge gaining during sessions. Relating these tactics to positive responses from candidates we aim at predicting successful strategies.

We have two new publications out, one on closing triads in social networks, and one on shaded walks (CoolWalks) in cities:

1. Exploring Time-Ordered Triadic Closure in Online Social Networks, by A. Galdeman, C. Tidiane Ba, M. Zignani, S. Gaito, published in ACM Transactions on the Web
   
   By analyzing networks with timestamped links from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an algorithm for the census of time-ordered triads, a vector-based model for representing growing networks (growth triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of closed triads. Our findings reveal significant variations in the impact of triadic closure across different OSNs, marked by diverse growth triadic profiles and varying formation speeds of closed triads as well as diversity in the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.
2. CoolWalks for active mobility in urban street networks, by H. Wolf, A.R. Vierø & M. Szell, published in Scientific Reports
   
   Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter alpha and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of alpha, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.

This week we are on fire and have yet another big paper out, long time in the making, led by Luca Maria Aiello with multi-NERDS involvement, just published in PNAS: Urban highways are barriers to social ties, by L.M. Aiello, A. Vybornova, S. Juhász, M. Szell, and E. Bokányi.

We have a big paper out today, long time in the making, led by Yanmeng Xin, our long-term PhD student visitor in 2021-2023, co-authored by Roberta Sinatra, just published in Nature Human Behavior: Academic mentees thrive in big groups, but survive in small groups, by Y. Xing, Y Ma, Y. Fan, R. Sinatra, and A. Zeng.

The main message of the paper is intriguing: If you “grow up” in a big research group, and if you survive, you will have high success. At the same time, in a big group it is also harder to survive, especially if your mentor is very productive. So what is then good mentorship, and what is a good group to be?

Interestingly, at NERDS we are a fairly big research group, but with several mentors who are by themselves not too busy, so we combine the best of both worlds 😁 In fact, this paper itself is another one in a long series of success stories where a visitor accomplished something great while staying at our ✨🦄 ~enchanted NERDS grounds~ 🧚‍♂️✨ in Copenhagen. (“NERDS is one of the best places I have ever stayed.”)

Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. Within the scientific community, possessing a large research group is often perceived as an indicator of exceptional mentorship and high-quality research. However, such large, competitive groups may also escalate dropout rates, particularly among early-career researchers. Overly high dropout rates of young researchers may lead to severe postdoc shortage and loss of top-tier academics in contemporary academia. In this context, we collect longitudinal genealogical data on mentor-mentee relations and their publication, and analyze the influence of a mentor’s group size on the future academic longevity and performance of their mentees. Our findings indicate that mentees trained in larger groups tend to exhibit superior academic performance compared to those from smaller groups, provided they remain in academia post-graduation. However, we also observe two surprising patterns: Academic survival rate is significantly lower for (1) mentees from larger groups, and for (2) mentees with more productive mentors. The trend is verified in institutions of different prestige. These findings highlight a negative correlation between a mentor’s success and the academic survival rate of their mentees, prompting a rethinking of effective mentorship and offering actionable insights for career advancement.

NERDS member Michele Coscia has updated his textbook for the Network Analysis and Advanced Network Science classes he teaches at ITU. This “Atlas for the Aspiring Network Scientist”, has now reached version 2.0, and 916 pages, and is available for anyone to read for free: https://arxiv.org/abs/2101.00863

Website: https://www.networkatlas.eu/

The new edition has a much improved coverage on graph neural networks, network data uncertainty, and background knowledge in statistics, machine learning, probability theory, and linear algebra.

Even version 2.0 has big margins for improvements. Please contact Michele with any comments.

Find a more detailed explanation of The Atlas for the Aspiring Network Scientist on Michele’s page: https://www.michelecoscia.com/?p=2393.

We have two new publications out, one on the Gamestop short squeeze by Reddit users, and one on bicycle network design with use case Copenhagen:

1. The dynamics of the Reddit collective action leading to the GameStop short squeeze, by A. Desiderio, L.M. Aiello, G. Cimini & L. Alessandretti , published in npj complexity
   
   In early 2021, the stock prices of GameStop, AMC, Nokia, and BlackBerry experienced dramatic increases, triggered by short-squeeze operations that have been largely attributed to Reddit’s retail investors. Here we shed light on the extent and timing of Reddit users’ influence on the GameStop short squeeze. Using statistical analysis tools with high temporal resolution, we find that increasing Reddit discussions anticipated high trading volumes. This effect emerged abruptly a few weeks before the event but waned once the community gained widespread visibility through Twitter. Meanwhile, the collective investment of the community, quantified through posts of individual positions, closely mirrored the market capitalization of the stock. This evidence suggests a coordinated action of users in developing a shared financial strategy through social media—targeting GameStop first and other stocks afterward. Overall, our results provide novel insights into the role of Reddit users in the dynamics of the GameStop short squeeze.
2. Cohesive urban bicycle infrastructure design through optimal transport routing in multilayer networks, by A. Lonardi, M. Szell and C. De Bacco, published in Journal of the Royal Society Interface
   
   Bicycle infrastructure networks must meet the needs of cyclists to position cycling as a viable transportation choice in cities. In particular, protected infrastructure should be planned cohesively for the whole city and spacious enough to accommodate all cyclists safely and prevent cyclist congestion—a common problem in cycling cities like Copenhagen. Here, we devise an adaptive method for optimal bicycle network design and for evaluating congestion criticalities on bicycle paths. The method goes beyond static network measures, using computationally efficient adaptation rules inspired by optimal transport on the dynamically updating multilayer network of roads and protected bicycle lanes. Street capacities and cyclist flows reciprocally control each other to optimally accommodate cyclists on streets with one control parameter that dictates the preference of bicycle infrastructure over roads. Applying our method to Copenhagen confirms that the city’s bicycle network is generally well-developed. However, we are able to identify the network’s bottlenecks, and we find, at a finer scale, disparities in network accessibility and criticalities between different neighbourhoods. Our model and results are generalizable beyond this particular case study to serve as a scalable and versatile tool for aiding urban planners in designing cycling-friendly cities.