Publications

• Gholamali Aminian, Andrew Elliott, Tiger Li, Timothy Cheuk Hin Wong, Victor Claude Dehon, Lukasz Szpruch, Carsten Maple, Christopher Read, Martin Brown, Gesine Reinert, Mo Mamouei (2026). FraudTransformer: Time-Aware GPT for Digital Payment Fraud Detection. In 2026 IEEE Conference on Artificial Intelligence (CAI).

• Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, Gesine Reinert (2026). GDP nowcasting with large-scale inter-industry payment data in real time: a network approach. Journal of the Royal Statistical Society Series A: Statistics in Society.

• Aaron A. Sandel, Yixuan He, Junpeng Ren, Yik Lun Kei, Kevin Lee, Isabelle R. Clark, Rachna Reddy, Jacob D. Negrey, Charles Birungi, Blessing A. Apamaku, Diana Kanweri, Davis Kalunga, Christopher Aliganyira, Sebastian Ramirez-Amaya, Phionah Nakayima, Raymond Katumba, Brian Kamugyisha, Daniela Acosta-Florez, Bas van Boekholt, Godfrey Mbabazi, Erone Akamumpa, Sharifah Namaganda, Alfred Tumusiime, Samuel Angedakin, Gesine Reinert, Oscar Hernan Madrid Padilla, Mihai Cucuringu, David Wipf, Kevin E. Langergraber, David P. Watts, John Mitani (2026). Lethal conflict after group fission in wild chimpanzees. Science.

• K. Balasubramanian, Murat A. Erdogdu, Larry Goldstein, Gesine Reinert (2026). Stein’s Method in Stochastic Geometry, Statistical Learning, and Optimisation. Oberwolfach Reports.

• Gesine Reinert (2026). Gesine Reinert’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al.. Journal of the Royal Statistical Society Series B (Statistical Methodology).

• Ruihua Zhang, Gesine Reinert (2025). A Duplication-Divergence Hypergraph Model for Protein Complex Data. Complexities.

• Tadas Temcinas, Vidit Nanda, Gesine Reinert (2025). Goodness-of-fit via count statistics in dense random simplicial complexes. Foundations of Data Science.

• Elly Hung, Anastasia Mantziou, Gesine Reinert (2025). A Bayesian mixture model for Poisson network autoregression. Social Network Analysis and Mining.

• Anum Fatima, Gesine Reinert (2025). Stein’s method for distributions modelling competing and complementary risk problems. Advances in Applied Probability.

• James Wilsenach, Charlotte M. Deane, Gesine Reinert, Katie Warnaby (2025). Graph models of brain state in deep anesthesia reveal sink state dynamics of reduced spatiotemporal complexity. Network Neuroscience.

• Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu (2024). Robust Angular Synchronization via Directed Graph Neural Networks. In International Conference on Learning Representations 2024 (ICLR 2024).

• Stratis Limnios, P.A. Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, Andrew Elliott (2024). SaGess: A Sampling Graph Denoising Diffusion Model for Scalable Graph Generation. Frontiers in artificial intelligence and applications.

• Ruihua Zhang, Gesine Reinert (2024). Simulating Weak Attacks in a New Duplication-Divergence Model with Node Loss. Entropy.

• Stephanie Armbruster, Gesine Reinert (2024). Network-based time series modeling for COVID-19 incidence in the Republic of Ireland. Applied Network Science.

• Yutong Lu, Gesine Reinert, Mihai Cucuringu (2024). Trade co-occurrence, trade flow decomposition and conditional order imbalance in equity markets. Quantitative Finance.

• Yixuan He, Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert (2024). PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs. In Proceedings of the Second Learning on Graphs Conference, PMLR 231.

• Ruikang Ouyang, Andrew Elliott, Stratis Limnios, Mihai Cucuringu, Gesine Reinert (2024). L2G2G: A Scalable Local-to-Global Network Embedding with Graph Autoencoders. Studies in computational intelligence.

• Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert (2023). The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights. Journal of Complex Networks.

• Tadas Temcinas, Vidit Nanda, Gesine Reinert (2023). Multivariate central limit theorems for random clique complexes. Journal of Applied and Computational Topology.

• Robert E. Gaunt, Gesine Reinert (2023). Bounds for the chi-square approximation of Friedman’s statistic by Stein’s method. Bernoulli.

• Guillaume Mijoule, Martin Raic, Gesine Reinert, Yvik Swan (2023). Stein’s density method for multivariate continuous distributions. Electronic Journal of Probability.

• Yixuan He, Michael Perlmutter, Gesine Reinert, Mihai Cucuringu (2022). MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian. In Proceedings of the First Learning on Graphs Conference, PMLR 198 (LoG 2022).

• Yixuan He, Gesine Reinert, Mihai Cucuringu (2022). DIGRAC: Digraph Clustering Based on Flow Imbalance. In Proceedings of the First Learning on Graphs Conference, PMLR 198 (LoG 2022).

• Yixuan He, Quanyu Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu (2022). GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. In Proceedings of the 39th International Conference on Machine Learning, PMLR 162 (ICML 2022).

• James Wilsenach, Catherine E. Warnaby, Charlotte M. Deane, Gesine Reinert (2022). Ranking of communities in multiplex spatiotemporal models of brain dynamics. Applied Network Science.

• Wenkai Xu, Gesine Reinert (2022). AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators. Advances in Neural Information Processing Systems 35.

• Wenkai Xu, Gesine Reinert (2022). A Kernelised Stein Statistic for Assessing Implicit Generative Models. Advances in Neural Information Processing Systems 35.

• Yixuan He, Gesine Reinert, Songchao Wang, Mihai Cucuringu (2022). SSSNET: Semi-Supervised Signed Network Clustering. Society for Industrial and Applied Mathematics eBooks.

• Stefanos Bennett, Mihai Cucuringu, Gesine Reinert (2022). Lead-lag detection and network clustering for multivariate time series with an application to the US equity market. Machine Learning.

• Andreas Anastasiou, Alessandro Barp, Francois-Xavier Briol, Bruno Ebner, Robert E. Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, Arthur Gretton, Christophe Ley, Qiang Liu, Lester Mackey, Chris J. Oates, Gesine Reinert, Yvik Swan (2022). Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments. Statistical Science.

• Max Fathi, Larry Goldstein, Gesine Reinert, Adrien Saumard (2022). Relaxing the Gaussian assumption in shrinkage and SURE in high dimension. The Annals of Statistics.

• Javier Pardo-Diaz, Philip S. Poole, Mariano Beguerisse-Diaz, Charlotte M. Deane, Gesine Reinert (2022). Generating weighted and thresholded gene coexpression networks using signed distance correlation. Network Science.

• Javier Pardo-Diaz, Mariano Beguerisse-Diaz, Philip S. Poole, Charlotte M. Deane, Gesine Reinert (2022). Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum. Journal of Computational Biology.

• Marie Ernst, Gesine Reinert, Yvik Swan (2022). On Papathanasiou’s covariance expansions. Latin American Journal of Probability and Mathematical Statistics.

• J.A. Cooper, Peter Mitic, Gesine Reinert, Tadas Temcinas (2022). Topological Analysis of Credit Data: Preliminary Findings. Lecture Notes in Computer Science.

• Fang Liu, Wenkai Xu, Jun Lu, Danica J. Sutherland (2021). Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

• Alexandre d’Aspremont, Mihai Cucuringu, Hemant Tyagi (2021). Ranking and synchronization from pairwise measurements via SVD. Journal of Machine Learning Research.

• A. D. Barbour, Gesine Reinert (2021). Estimating the correlation in network disturbance models. Journal of Complex Networks.

• Wenkai Xu, Takuo Matsuda (2021). Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds. In Proceedings of the 38th International Conference on Machine Learning, PMLR 139 (ICML 2021).

• Wenkai Xu, Gesine Reinert (2021). A Stein Goodness-of-fit Test for Exponential Random Graph Models. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, PMLR 130 (AISTATS 2021). (code)

• Javier Pardo-Diaz, Lyuba V. Bozhilova, Mariano Beguerisse-Diaz, Philip S. Poole, Charlotte M. Deane, Gesine Reinert (2021). Robust gene coexpression networks using signed distance correlation. Bioinformatics.