Publications

• He Y, Perlmutter M, Reinert G, Cucuringu M (2022) MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed LaplacianIn Proceedings of the First Learning on Graphs Conference, PMLR 198:40:1-40:39 (LoG 2022)

• He Y, Reinert G, Cucuringu M (2022) DIGRAC: Digraph Clustering Based on Flow ImbalanceIn Proceedings of the First Learning on Graphs Conference, PMLR 198:21:1-21:43 (LoG 2022)

• He Y, Gan Q, Wipf D, Reinert G, Yan J, Cucuringu M (2022) GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks In Proceedings of the 39 th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022 (ICML 2022)

• Wilsenach J, Warnaby C, Deane C, Reinert G, (2022). Ranking of communities in multiplex spatiotemporal models of brain dynamics. Applied Network Science

• Xu W, (2022). Standardisation-function Kernel Stein Discrepancy (Sf-KSD): A Unifying Approach for Non-parametric Goodness-of-fit Testing. In Proceedings of 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)

• Xu W, Reinert G, (2022) AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators. Submitted (code)

• He Y, Gesine Reinert G, Wang S, Cucuringu M (2022). SSSNET: Semi-Supervised Signed Network Clustering

• He Y, Zhang X, Huang J, Cucuringu M, Reinert G (2022). PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs (Software)

• Temčinas T, Nanda V, Reinert G, Multivariate Central Limit Theorems for Random Clique Complexes Submitted

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

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

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

• Xu W, Matsuda T, (2021). Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds. In Proceedings of 38th International Conference on Machine Learning (ICML2021)

• Stefanos Bennett , Cucuringu, M., Reinert, G.. (2021). Detection and clustering of lead-lag networks for multivariate time series with an application to financial markets. preliminary version appeared at KDD 2021 - 7th SIGKKDD Workshop on Mining and Learning from Time Series (MiLeTS)

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

• Pardo-Diaz J, Bozhilova L, Beguerisse-Díaz M, Poole P, Deane C, Reinert G, (2021). Robust gene coexpression networks using signed distance correlation. Bioinformatics (Oxford, England)