Preprints

  • C. Kodsi and D. Pasadakis. “Nonlinear Modified PageRank Problem for Local Graph Partitioning,” September 2025, arXiv link code.

  • D. Pasadakis, R. S. Steiner, P. A. Papp, T. Böhnlein, A.-J. Yzelman. “Symmetry-breaking symmetry in directed spectral partitioning,” September 2025, arXiv link code.

Journal articles

  • D. V. Rodriguez, S. Omlin, D. Pasadakis, and O. Schenk, “Generating Architecture-Agnostic Performance Tests from Functional Unit Tests”, ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 11, 1–2, Article 16, 2026, doi: 10.1145/3801098, code.

  • A. Eftekhari, L. Gaedke-Merzhäuser, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, “Algorithm 1042: Sparse Precision Matrix Estimation With SQUIC,” ACM Transactions of Mathematical Software, March 2024, doi: 10.1145/3650108, code.

  • D. Pasadakis, M. Bollhöfer, and O. Schenk, “Sparse quadratic approximation for graph learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11256-11269, 1 Sept. 2023, doi: 10.1109/TPAMI.2023.3263969 and TechRxiv link.

  • D. Pasadakis, C.L. Alappat, O. Schenk, and G. Wellein, “Multiway p-spectral graph cuts on Grassmann manifolds,” Machine Learning 111, 791–829, 2022, doi: 10.1007/s10994-021-06108-1, code.

  • A. Eftekhari, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, “Block-enhanced precision matrix estimation for large-scale datasets,” Journal of Computational Science, Volume 53, 2021, doi: 10.1016/j.jocs.2021.101389.

Conference papers

  • M. Lechekhab, D. Pasadakis, Roger Käppeli, A. Eftekhari, and O. Schenk, ``GraphLab.jl: A Julia Framework for Graph Partitioning,’’ The Proceedings of the JuliaCon Conferences, Accepted, in press, 2026.

  • L. Losavio, L. Persia, M. Sathe, and D. Pasadakis, “Fraud detection in cryptocurrency markets with spatio-temporal graph neural networks,” in 2026 IEEE Swiss Conference on Data Science and AI (SDS), Zurich, Switzerland, 2026, pp. 123-131, doi: 10.1109/SDS70563.2026.00024, code

  • X. Niu, G. Meyer, D. Pasadakis, A. -J. Yzelman and O. Schenk, “Incremental Sparse Tensor Format for Maximizing Efficiency in Tensor-Vector Multiplications,” 2025 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops), Edinburgh, United Kingdom, 2025, doi: 10.1109/CLUSTERWorkshops65972.2025.11164206. Poster, Best poster award.

  • M. Lechekhab, D. Pasadakis, and O. Schenk, “Multilevel diffusion based spectral graph clustering,” in 2024 IEEE High Performance Extreme Computing Conference (HPEC), Wakefield, MA, USA, pp. 1-7, 2024,. Outstanding paper award. doi: 10.1109/HPEC62836.2024.10938528

  • J. Schmidt, D. Pasadakis, M. Sathe, and O. Schenk, “GAMLNet: a graph based framework for the detection of money laundering,” 2024 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 2024, pp. 241-245, doi: 10.1109/SDS60720.2024.00043. poster, Best poster award, code.

  • V.I. Makri and D. Pasadakis, “The clustering of source rocks: A spectral approach”. in Mediterranean Geosciences Union, MedGU, 20 March 2024. doi: 10.1007/978-3-031-48758-3_72. Best paper award, code

  • D. Pasadakis, O. Schenk, V. Vlacic, and A.-J. Yzelman, “Nonlinear spectral clustering with C++ GraphBLAS,” in 2023 EEE High Performance Extreme Computing Conference (HPEC), Wakefield, MA, USA, 2023. Outstanding short paper award. doi: 10.48550/arXiv.2605.26975 poster

  • V.I. Makri, D. Pasadakis, and N. Pasadakis, “A novel chemometric approach for oil & source rock clustering,” in European Association of Geoscientists & Engineers, pp. 1-2, 2023, doi: 10.3997/2214-4609.202333183.

  • T. Simpson, D. Pasadakis, D. Kourounis, K. Fujita, T. Yamaguchi, T. Ichimura, and O. Schenk, “Balanced graph partition refinement using the graph p-Laplacian,” in Proceedings of the Platform for Advanced Scientific Computing Conference, Association for Computing Machinery, New York, NY, USA, PASC ’18, 2018, doi: 10.1145/3218176.3218232.

Thesis

  • D. Pasadakis, “Learning and clustering graphs from high dimensional data,” PhD thesis, Università della Svizzera italiana, 2023. Thesis