Giona Casiraghi

Last Name

Casiraghi

First Name

Giona

ORCID

0000-0003-0233-5747

Organisational unit

06338 - KOF FB KOF Lab / KOF FB KOF Lab

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Publications1 - 10 of 42

Locating community smells in software development processes using higher-order network centralities
Gote, Christoph; Perri, Vincenzo; Zingg, Christian; et al. (2023)
Social Network Analysis and Mining
Community smells are negative patterns in software development teams’ interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams’ interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method’s success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team’s development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.
The nonlinear economy: How resource constraints lead to business cycles
Schweitzer, Frank; Casiraghi, Giona (2025)
Chaos
We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.
Generalised hypergeometric ensembles of random graphs: The configuration model as an urn problem
Casiraghi, Giona; Nanumyan, Vahan (2018)
arXiv
We introduce a broad class of random graph models: the generalised hypergeometric ensemble (GHypEG). This class enables to solve some long-standing problems in random graph theory. First, GHypEG provides an elegant and compact formulation of the well-known configuration model in terms of an urn problem. Second, GHypEG allows incorporating arbitrary tendencies to connect different vertex pairs. Third, we present the closed-form expressions of the associated probability distribution ensures the analytical tractability of our formulation. This is in stark contrast with the previous state-of-the-art, which is to implement the configuration model by means of computationally expensive procedures.
Quantifying triadic closure in multi-edge social networks
Brandenberger, Laurence; Casiraghi, Giona; Nanumyan, Vahan; et al. (2019)
Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '19
In social networks, edges often form closed triangles or triads. Standard approaches to measuring triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks based on a shared partner statistic and demonstrate that this measure can detect meaningful closure in synthetic and empirical multi-edge networks, where conventional approaches fail. This work is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.
Detecting Path Anomalies in Time Series Data on Networks
LaRock, Timothy; Nanumyan, Vahan; Scholtes, Ingo; et al. (2019)
arXiv
The unsupervised detection of anomalies in time series data has important applications, e.g., in user behavioural modelling, fraud detection, and cybersecurity. Anomaly detection has been extensively studied in categorical sequences, however we often have access to time series data that contain paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies we must account for the fact that such data contain a large number of independent observations of short paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem we introduce a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph, which provides an efficient analytical method to detect paths with anomalous frequencies th at result from nodes being traversed in unexpected chronological order.
Reconstructing signed relations from interaction data
Andres, Georges; Casiraghi, Giona; Vaccario, Giacomo; et al. (2022)
arXiv
Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in four communities. We then show that these relations correspond to the ones reported in surveys. Additionally, the inferred relations allow us to study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. We evaluate the importance of triads in the signed network to study group cohesion.
Shock! Quantifying the Impact of Core Developers’ Dropout on the Productivity of OSS Projects
Latona, Giuseppe Russo; Gote, Christoph; Zingg, Christian; et al. (2024)
Proceedings of the World Wide Web Conference 2024 (WWW '24)
Open Source Software (OSS) projects play a critical role in the digital infrastructure of companies and services provided to millions of people. Given their importance, understanding the resilience of OSS projects is paramount. A primary reason for OSS project failure is the shock caused by the dropout of a core developer, which can jeopardize productivity and project survival. Using a difference-in-differences (DiD) analysis, this study investigates the repercussions of this shock on the productivity of 8,234 developers identified among 9,573 OSS GitHub projects. Our findings reveal the indirect impact of the core developer’s dropout. The remaining developers experienced a 20% productivity drop. This observation is troubling because it suggests that the shock might push other developers to drop out, putting the collaboration structure of the project at risk. Also, projects with higher productivity before the shock experienced a larger drop-down after the shock. This points to a tradeoff between productivity and resilience, i.e., the ability of OSS projects to recover from the dropout of a core developer. Our findings underscore the importance of a balanced approach in OSS project management, harmonizing productivity goals with resilience considerations.
Supply-chain vulnerabilities in critical medicines: A persistent risk to pharmaceutical security
Casiraghi, Giona; Andres, Georges; Schweitzer, Frank; et al. (2025)
Science
Understanding Online Migration Decisions Following the Banning of Radical Communities
Russo, Giuseppe; Horta Ribeiro, Manoel; Casiraghi, Giona; et al. (2023)
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
The proliferation of radical online communities and their violent offshoots has sparked great societal concern. However, the current practice of banning such communities from mainstream platforms has unintended consequences: (i)\ the further radicalization of their members in fringe platforms where they migrate; and (ii)\ the spillover of harmful content from fringe back onto mainstream platforms. Here, in a large observational study on two banned subreddits, r/The_Donald and r/fatpeoplehate, we examine how factors associated with the RECRO radicalization framework relate to users migration decisions. Specifically, we quantify how these factors affect users decisions to post on fringe platforms and, for those who do, whether they continue posting on the mainstream platform. Our results show that individual-level factors, those relating to the behavior of users, are associated with the decision to post on the fringe platform. Whereas social-level factors, users connection with the radical community, only affect the propensity to be coactive on both platforms. Overall, our findings pave the way for evidence-based moderation policies, as the decisions to migrate and remain coactive amplify unintended consequences of community bans.
Adapting to disruptions: Managing supply chain resilience through product rerouting
Amico, Ambra; Verginer, Luca; Casiraghi, Giona; et al. (2024)
Science Advances
Supply chain disruptions may cause shortages of essential goods, affecting millions of individuals. We propose a perspective to address this problem via reroute flexibility. This is the ability to substitute and reroute products along existing pathways, hence without requiring the creation of new connections. To showcase the potential of this approach, we examine the US opioid distribution system. We reconstruct over 40 billion distribution routes and quantify the effectiveness of reroute flexibility in mitigating shortages. We demonstrate that flexibility (i) reduces the severity of shortages and (ii) delays the time until they become critical. Moreover, our findings reveal that while increased flexibility alleviates shortages, it comes at the cost of increased complexity: We demonstrate that reroute flexibility increases alternative path usage and slows down the distribution system. Our method enhances decision-makers' ability to manage the resilience of supply chains.

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Giona Casiraghi

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