Comparing the Italian and Danish Music Industries With Network Analysis by L.M. Benetti, D. Fejerskov-Quist, & M. Coscia, published in Complexity

We build music band collaboration networks based on bands sharing artists playing for them. We do so for both Italy and Denmark. The paper shows that the two networks have similar structural properties, with clusters based on genres and temporal similarity. There are also differences, showing that the Danish and Italian music industries have different periods and genres in which they express a high degree of diversity. Finally, we can identify the bands that link together the Italian and Danish music scene.

Right-wing Votes Relate to Delays in Bicycle Network Development, by C. Sebastiao & M. Szell, published in Findings

Paris has implemented only 43% of its 2021-2026 bicycle network plan by its 2026 deadline, but the delays are not equally distributed. Correlating political voting and bicycle infrastructure data, we find that boroughs with a higher share of right-wing votes are also boroughs with the largest delays in bicycle infrastructure development. Although the number of voting units is only 17, this correlation is remarkably strong and significant. At the city scale, for each additional 1% of right-wing votes, we find 0.86% less protected bicycle lanes built.

Human, Algorithm, or Both? Gender Bias in Human-Augmented Recruiting, by Mesut Kaya & Toine Bogers, published in FAccT ’26: The 2026 ACM Conference on Fairness, Accountability, and Transparency

Recent years have seen rapid growth in the market for HR technology and AI-driven HR solutions in particular. This popularity has also resulted in increased attention to the negative aspects of using AI to support hiring practices, such as the risk of reinforcing existing biases against vulnerable groups based on gender or other sensitive attributes. Combining human experience with AI efficiency in making recruiting and selection decisions has the potential to help mitigate these biases, but despite a considerable amount of research on fairness in algorithmic hiring, actual empirical evaluations comparing the fairness of human, AI, and human-augmented decision-making remain scarce. In this study, we address this gap by presenting a quantitative analysis of gender bias across three scenarios of a real-world recruitment platform: (1) recruiters searching a CV database manually for relevant candidates, (2) AI-driven matching between candidates and jobs, and (3) a combination of human and AI-driven recruiting. We find that human recruiters produce lists of candidates that are fairer in terms of gender than the AI-only solution, with more deliberation by humans resulting in fairer outcomes. However, the combination of human and AI-driven is more than the sum of its parts and produces the fairest candidate lists: interacting with the slate of recommended candidates first before manually searching for additional candidates has a beneficial effect on the gender fairness of the set of candidates that are viewed, clicked, and contacted afterwards. Our work provides one of the first empirical comparisons of fairness across human, AI, and hybrid recruiting processes, offering evidence to inform the development of more equitable hiring practices and highlighting the importance of human oversight for mitigating bias in algorithmic hiring.

Displacement and disconnection: the impact of violence on migration networks and highway traffic in Mexico, by M. Coscia & R. Gutiérrez-Romero, published in Spatial Economic Analysis.

This paper examines how violence impacts migration flows and the strength of migration networks across Mexico’s 2454 municipalities. Using a novel network algorithm and census data from 2005 to 2020, we detect structural changes in domestic and international migration beyond what net flows reveal. To identify causal effects, homicide rates are instrumented using variation in fuel prices and municipal distance to fuel pipelines, capturing exogenous shocks from large-scale fuel theft. Rising violence led to 1.12 million additional domestic emigrants, 50,200 fewer returnees from the United States, stronger emigration networks and reduced highway traffic linking violent areas to the rest of the country.

The trade-off between directness and coverage in transport network growth, by C. Sebastiao, A. Vybornova, A.R. Vierø, L.M. Aiello & M. Szell, published in Applied Network Science

We systematically study the growth of connected planar networks, quantifying functionality of the growing network structure. We compare random growth with various greedy and human-designed, manual growth strategies. We evaluate our results via the fundamental performance metrics of directness and coverage, finding non-trivial trade-offs between them. Manual strategies fare better than greedy strategies on both metrics, while random strategies perform worst and are unlikely to be Pareto efficient. Centrality-based greedy strategies tend to perform best for directness but are worse than random strategies for coverage, while coverage-based greedy strategies can achieve maximum global coverage as fast as possible but perform as poorly for directness as random strategies. Directness-based greedy strategies get stuck in local optimum traps. These results hold for a number of stylized urban transport network topologies. Our insights are crucial for applications where the order in which links are added to a spatial network is important, such as in urban or regional transport network design problems.

We have two new publications out!

1. Disconnect between the public face and the voting behavior of political representatives by Christian Ivert Andersen and Michele Coscia, published in the journal Applied Network Science.
   
   One of representative democracy’s tenets is that a political candidate runs on a specific platform, which is information the electorate uses to determine whether to vote for them or not. If this promise is to be maintained, it is fundamental that the public face candidates present corresponds to their actions in parliament once elected. Such a promise has been put in question both by scholars, but also by the electorate. In different countries at different times, the people have expressed various degrees of dissatisfaction with democracy: often the feeling is that representatives put their own interests—or the interest of a powerful minority—before the ones of their constituencies. In this paper, we propose a network-based quantitative investigation of this disconnect between the public face and the voting behavior of elected representatives. By using data from Denmark, we can place politicians in two different spaces, determined by their electoral campaign promises on the one hand, and on the other hand by the votes they cast in parliament. We find that our technique makes it possible both to find clear, expected, and consistent left-right divides between the political parties; as well as a larger-than-expected disconnect between the public face and the voting behavior. Our preliminary results indicate that the aggregate voting behavior in parliament of politicians does not match with how they present themselves to the public on the salient issues discussed during the election campaign.
2. Evaluating fraud detection algorithms in a decentralized scenario by Ada M Gige, Lasse Buschmann Alsbirk, Michele Coscia, published in the journal Royal Society Open Science.
   
   Financial fraud is an umbrella term including a vast number of illegal activities. These activities involve a significant fraction of the global economy. Traditional investigation techniques are labour-intensive and cannot scale to match the size of the issue. Machine learning has provided effective tools which deliver high accuracy in identifying transactions that could be involved in fraudulent activities. In this paper, we point out that the state-of-the-art in financial fraud detection has been applied to the unrealistic scenario of an omniscient centralized global authority which has access to all bank transactions globally. We propose a more realistic evaluation scenario, one made of two steps: first, the bank flags its own transactions using exclusively information it possesses; then only flagged transactions from all banks are analysed by the governmental authority for potential prosecution. We find that, in such a realistic scenario, the effectiveness of the state-of-the-art method for financial fraud detection decreases. Moreover, we show that in this decentralized scenario, it pays off to use simpler methods than the state-of-the-art, depending on the specific objective function the system wants to ensure.

We have two new publications out!

1. Detecting bias in algorithms used to disseminate information in social networks and mitigating it using multiobjective optimization, by Vedran Sekara, Ivan Dotu, Manuel Cebrian, Esteban Moro, Manuel Garcia−Herranz published in PNAS Nexus
   
   Based on extensive computer simulations on synthetic and 10 diverse real-world social networks we show that seeding information in social networks using state-of-the-art influence maximization methods creates information gaps. Our results show that these algorithms select influencers who do not disseminate information equitably, threatening to create an increasingly unequal society. To overcome this issue, we devise a multiobjective algorithm which both maximizes influence and information equity. Our results demonstrate it is possible to reduce vulnerability at a relatively low trade-off with respect to spread. This highlights that in our search for maximizing the spread of information we do not need to compromise on information equality.
2. Adaptive continuity-preserving simplification of street networks, by Martin Fleischmann, Anastassia Vybornova, James D. Gaboardi, Anna Brázdová, Daniela Dančejová, published in Computers, Environment and Urban Systems
   
   Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification — the process of converting a highly granular input network into its most simple morphological form — is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm — available in the neatnet Python package — outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.
   See also the neatnet package: https://github.com/uscuni/neatnet

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

1. Estimating affective polarization on a social network, by Marilena Hohmann and Michele Coscia, published in PLOS ONE.
   
   Concerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently. We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in the following months.
   See also Michele’s blog post: https://www.michelecoscia.com/?p=2466
2. Leveraging VLLMs for Visual Clustering: Image-to-Text Mapping Shows Increased Semantic Capabilities and Interpretability, by Luigi Arminio, Matteo Magnani, Matías Piqueras, Luca Rossi, and Alexandra Segerberg published in Social Science Computer Review.
   
   We test an approach that leverages the ability of Vision-and-Large-Language-Models (VLLMs) to generate image descriptions that incorporate connotative interpretations of the input images. In particular, we use a VLLM to generate connotative textual descriptions of a set of images related to climate debate, and cluster the images based on these textual descriptions. In parallel, we cluster the same images using a more traditional approach based on CNNs. In doing so, we compare the connotative semantic validity of clusters generated using VLLMs with those produced using CNNs, and assess their interpretability. The results show that the approach based on VLLMs greatly improves the quality score for connotative clustering. Moreover, VLLM-based approaches, leveraging textual information as a step towards clustering, offer a high level of interpretability of the results.
3. Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring, by Mesut Kaya and Toine Bogers, published in RecSys ’25: Proceedings of the Nineteenth ACM Conference on Recommender Systems
   
   Past analyses of fairness in algorithmic hiring have been restricted to single-side fairness, ignoring the perspectives of the other stakeholders. In this paper, we address this gap and present a multi-stakeholder approach to fairness in a candidate recommender system that recommends relevant candidate CVs to human recruiters in a human-in-the-loop algorithmic hiring scenario. We conducted semi-structured interviews with 40 different stakeholders (job seekers, companies, recruiters, and other job portal employees). We used these interviews to explore their lived experiences of unfairness in hiring, co-design definitions of fairness as well as metrics that might capture these experiences. Finally, we attempt to reconcile and map these different (and sometimes conflicting) perspectives and definitions to existing (categories of) fairness metrics that are relevant for our candidate recommendation scenario.