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University of Fribourg

Academic institutioneurope · ch
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Research library20linked papers
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Selected work

Representative Papers

A Centrality Measure Using Magnitude Homology

Jul 17, 2026

This study addresses the problem of effectively measuring node centrality in graphs from geometric and topological perspectives. To this end, it introduces magnitude homology—a novel application in graph centrality analysis—and proposes a local centrality measure grounded in relative homology: the importance of a node is quantified by the change in magnitude homology resulting from its removal. The proposed measure satisfies several natural axioms, exhibits favorable theoretical properties, and demonstrates unique effectiveness in experiments, offering complementary insights to classical centrality metrics. This work thus provides a new topological lens for evaluating node importance in complex networks.

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When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

Jul 16, 2026

The widespread adoption of generative AI blurs the boundaries of users’ actual contributions in creative processes, often leading to misperceptions of authorship. This work introduces the novel concept of “authorship calibration”—defined as users’ accurate self-assessment of their genuine contribution in human-AI collaboration—and presents an empirical analysis based on the CoAuthor dataset. The study reveals that frequent AI users systematically overestimate their own input, whereas infrequent users exhibit more accurate calibration, thereby uncovering a link between AI usage intensity and metacognitive bias. These findings offer a new theoretical lens and empirical foundation for understanding how generative AI reshapes human perceptions of creative agency and authorship.

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Recent publications

Latest Papers

A Centrality Measure Using Magnitude Homology

Jul 17, 2026

This study addresses the problem of effectively measuring node centrality in graphs from geometric and topological perspectives. To this end, it introduces magnitude homology—a novel application in graph centrality analysis—and proposes a local centrality measure grounded in relative homology: the importance of a node is quantified by the change in magnitude homology resulting from its removal. The proposed measure satisfies several natural axioms, exhibits favorable theoretical properties, and demonstrates unique effectiveness in experiments, offering complementary insights to classical centrality metrics. This work thus provides a new topological lens for evaluating node importance in complex networks.

0 citationsRead paper

When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

Jul 16, 2026

The widespread adoption of generative AI blurs the boundaries of users’ actual contributions in creative processes, often leading to misperceptions of authorship. This work introduces the novel concept of “authorship calibration”—defined as users’ accurate self-assessment of their genuine contribution in human-AI collaboration—and presents an empirical analysis based on the CoAuthor dataset. The study reveals that frequent AI users systematically overestimate their own input, whereas infrequent users exhibit more accurate calibration, thereby uncovering a link between AI usage intensity and metacognitive bias. These findings offer a new theoretical lens and empirical foundation for understanding how generative AI reshapes human perceptions of creative agency and authorship.

0 citationsRead paper