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ENBIS-26 Conference

Sep 6 – 10, 2026
Centro Didattico Morgagni
Europe/Rome timezone

Chair of the Local Organising Committee

Session

ISBIS Session - Advances in Statistical Analysis for Large-Scale Dynamic Networks, Labour Market Outcomes, and Interpretable Ensemble Methods

Sep 8, 2026, 9:00 AM

Conveners

ISBIS Session - Advances in Statistical Analysis for Large-Scale Dynamic Networks, Labour Market Outcomes, and Interpretable Ensemble Methods

  • Antonio Lepore (Università degli Studi di Napoli Federico II - Dept. of Industrial Engineering)

Presentation materials

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  1. 108. Real-Time Change Detection in Large-Scale Dynamic Networks
    Nathaniel Stevens (University of Waterloo)
    9/8/26, 9:00 AM
    Other/special session/invited session

    In this talk we present a real-time change detection method for monitoring large, dynamic networks with community structure. We model the propensity for communication within and between communities to incorporate the structure of the underlying network. Our focus on communities makes our method scalable to large-scale networks and we use a window-based approach to accommodate network dynamics...

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  2. 93. Heterogeneity in STEM Graduate Wages Across Italian Universities: A Weighted Mixed-Effects Analysis
    Andrea Carta (university of Cagliari)
    9/8/26, 9:30 AM
    Other/special session/invited session

    This study examines wage outcomes at the cohort level among STEM graduates in Italy, using data from the AlmaLaurea surveys covering 60 institutions over the period 2008-2023. The unit of analysis is a graduate cohort sharing the same university, degree level, and disciplinary category, observed one, three, or five years after completing their studies. Given the nested structure of the data...

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  3. 101. Agreement over Association in Explainable Ensemble Trees
    Agostino Gnasso (University of Naples Federico II)
    9/8/26, 10:00 AM
    Other/special session/invited session

    Ensemble methods such as Random Forests achieve strong predictive accuracy but at the cost of interpretability. Explainable Ensemble Trees (E2Tree) address this trade-off by constructing a single interpretable tree that approximates the co-occurrence structure induced by the ensemble. The quality of this approximation matters: when interpretability is invoked for regulatory or scientific...

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