Corinna Coupette, Alipasha Montaseri, and Christoph Lenzen. Model-Agnostic Approximation of Constrained Forest Problems. Preprint, submitted.
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We present the shell-decomposition algorithm, a model-agnostic meta-algorithm that efficiently computes a $(2+Ɛ)$-approximation to Constrained Forest Problems for a broad class of forest functions describing network design problems with edge subsets as solutions.

Jeremy Wayland, Corinna Coupette†, and Bastian Rieck†. Mapping the Multiverse of Latent Representations. International Conference on Machine Learning (ICML), to appear.
A*, 27.5% acceptance rate.
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We introduce Presto, a topological framework to explore and exploit representational variability in latent-space machine-learning models.

Corinna Coupette, Stefan Neumann, and Aristides Gionis. Reducing Exposure to Harmful Content via Graph Rewiring. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 323–334.
A*, 22.1% acceptance rate.
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We introduce Gamine, a fast greedy algorithm for reducing the exposure to harm in recommendation graphs via edge rewiring, based on the theory of absorbing random walks.

Corinna Coupette, Sebastian Dalleiger, and Bastian Rieck. Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. International Conference on Learning Representations (ICLR).
A*, 31.8% acceptance rate.
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We develop Orchid, a flexible framework generalizing Ollivier-Ricci curvature to hypergraphs, prove that the resulting curvatures have favorable theoretical properties, and demonstrate that they are both scalable and useful to perform a variety of hypergraph tasks in practice.

Corinna Coupette*, Sebastian Dalleiger*, and Jilles Vreeken. Differentially Describing Groups of Graphs. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 3959–3967.
A*, 15% acceptance rate (oral ~5.5%).
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Given a set of graphs and a partition of these graphs into groups, we introduce Gragra (Graph group analysis) to discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related.

Corinna Coupette, Jyotsna Singh, and Holger Spamann. Simplify Your Law: Using Information Theory to Deduplicate Legal Documents. Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW), 631–638.
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We introduce the duplicated phrase detection problem for legal texts and propose the Dupex (Duplicated phrase extractor) algorithm to solve it, leveraging the Minimum Description Length principle to identify a set of duplicated phrases that together best compress the input text.

Corinna Coupette and Jilles Vreeken. Graph Similarity Description: How Are These Graphs Similar?. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 185–195.
A*, 15.4% acceptance rate.
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We treat graph similarity assessment as a description problem, rather than as a measurement problem. Having formalized this problem as a model selection task using the Minimum Description Length principle, we propose Momo (Model of models), which solves the problem by breaking it into two parts and introducing efficient algorithms for each.

Corinna Coupette and Christoph Lenzen. A Breezing Proof of the KMW Bound. Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA), 184–195.
29.4% acceptance rate.
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We give a simple and (in the extended version) fully self-contained proof of the KMW lower bound, proving a hardness result for several fundamental graph problems in the LOCAL model of distributed computing.

Tarmo Nurmi, Arash Badie-Modiri, Corinna Coupette, and Mikko Kivelä. pymnet: A Python Library for Multilayer Networks. Journal of Open Source Software 9(99), 6930.
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We introduce pymnet, a Python package providing essential data structures and computational tools for multilayer-network analysis and visualization.

Corinna Coupette, Dirk Hartung, and Daniel Martin Katz. Legal Hypergraphs. Philosophical Transactions of the Royal Society A, 20230141.
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We introduce temporal hypergraphs as representations of legal network data, demonstrating their utility in case studies on legal citation networks and legal collaboration networks.

Corinna Coupette, Jilles Vreeken, and Bastian Rieck. All the World's a (Hyper)Graph: A Data Drama. Digital Scholarship in the Humanities, 74–96.
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Raw data stem from all of Shakespeare’s plays / We model them as graphs in many ways / And demonstrate representations matter.

Corinna Coupette and Dirk Hartung. Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies. MIT Computational Law Report.
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Building on the scientific literature regarding reproducible research and peer review, we introduce seven foundational principles for creating a culture of constructive criticism in the transdisciplinary field of computational legal studies.

Corinna Coupette, Janis Beckedorf, Dirk Hartung, Maximilian Böther, and Daniel Martin Katz. Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting. Artificial Intelligence and Law 31, 335–368.
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Building on the computer science concept of code smells, we initiate the systematic study of law smells (i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law), introduce a comprehensive law smell detection toolkit, and demonstrate its utility on twenty-two years of legislation from the United States Code.

Corinna Coupette and Dirk Hartung. Rechtsstrukturvergleichung [Structural Comparative Law]. RabelsZ–The Rabel Journal of Comparative and International Private Law 86.4, 935–975.
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Theoretically grounded in systems theory and complexity science, we propose structural comparative law as a data-driven approach to explore the similarities and differences between the structures of legal systems.

Corinna Coupette*, Janis Beckedorf*, Dirk Hartung, Michael Bommarito, and Daniel Martin Katz. Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany. Frontiers in Physics 9, 269:1–269:31.
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We present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks and demonstrate its utility by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998–2019).

Daniel Martin Katz, Corinna Coupette, Janis Beckedorf, and Dirk Hartung. Complex Societies and the Growth of the Law. Scientific Reports 10, 18737:1–18737:14.
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We examine 25 years of statutory legislation in the United States and Germany through the lens of network science, finding that the main driver behind the growth of the law in both jurisdictions is the expansion of the welfare state, backed by an expansion of the tax state.

Corinna Coupette and Andreas M. Fleckner. Das Wertpapierhandelsgesetz (1994–2019): Eine quantitative juristische Studie [The Securities Trading Act (1994–2019): A Quantitative Legal Study]. Festschrift 25 Jahre WpHG, 53–85.
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A legal data science project investigating the evolution of Germany’s Securities Trading Act over the first 25 years of its lifetime.

Corinna Coupette and Andreas M. Fleckner. Quantitative Rechtswissenschaft [Quantitative Legal Studies]. Juristenzeitung 73, 379–389.
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We explain what legal data analysis is and discuss how German legal research could profit from it.

Corinna Coupette. Beyond Flatland: Exploring Graphs in Many Dimensions. SULB, XXI, 237 p.
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My computer-science dissertation which, based on my KDD 2021, AAAI 2022, DSH 2023, ICLR 2023, and KDD 2023 publications, explores graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility.

Corinna Coupette. Juristische Netzwerkforschung (Legal Network Science). Tübingen: Mohr Siebeck, XVIII, 376 p.
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My legal dissertation. I introduce network science to the German legal discourse and explore what legal network science could mean. This is how I got into graphs.

Bastian Rieck and Corinna Coupette. Evaluating the "Learning on Graphs" Conference Experience. Preprint, arXiv.2306.00586.
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We present the results of a survey distributed to participants of the first “Learning on Graphs” conference.