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Matthias Paul Lanzinger

Assistant Prof. Dipl.-Ing. Dr.techn. / BSc

Research Areas

  • Algorithms, Graph Neural Networks, Problem decomposition techniques based on graphs and hypergraphs, Parameterized Complexity, Computational Logic, Theoretical Compurter Science, Logic Programming
Matthias Paul Lanzinger

Role

Contact

2026W

2027S

 

2026

  • FPT Approximation of Generalised Hypertree Width for Bounded Intersection Hypergraphs / Lanzinger, M., & Razgon, I. (2026). FPT Approximation of Generalised Hypertree Width for Bounded Intersection Hypergraphs. ACM Transactions on Computation Theory, 18(2), 1–34. https://doi.org/10.1145/3799232
    Project: DeConquer (2023–2027)
  • Query Answering without Join Computation: An Interactive Exploration of Practical Techniques / Lanzinger, M., Pichler, R., & Selzer, A. (2026). Query Answering without Join Computation: An Interactive Exploration of Practical Techniques. In C. Binnig, S. Roy, J. Haritsa, & S. Sudarshan (Eds.), SIGMOD Companion ’26 : Companion of the International Conference on Management of Data (pp. 66–69). Association for Computing Machinery, Inc. (ACM). https://doi.org/10.1145/3788853.3801583
    Download: Paperpresentation (1.68 MB)
    Project: DeConquer (2023–2027)
  • FPT Parameterisations of Fractional and Generalised Hypertree Width / Lanzinger, M., Razgon, I., & Unterberger, D. (2026). FPT Parameterisations of Fractional and Generalised Hypertree Width. Proceedings of the ACM on Management of Data (PACMMOD), 4(2), 1–17. https://doi.org/10.1145/3801900
    Project: DeConquer (2023–2027)
  • A Logical View of GNN-Style Computation and the Role of Activation Functions / Barceló, P., Geerts, F., Lanzinger, M., Pakhomenko, K., & Van den Bussche, J. (2026). A Logical View of GNN-Style Computation and the Role of Activation Functions. Proceedings of the ACM on Management of Data (PACMMOD), 4(2), 1–19. https://doi.org/10.1145/3801914
    Project: DeConquer (2023–2027)
  • Selective Use of Yannakakis' Algorithm for Consistent Performance Gains / Böhm, D., Gottlob, G., Lanzinger, M., Longo, D. M., Okulmus, C., Pichler, R., & Selzer, A. (2026). Selective Use of Yannakakis’ Algorithm for Consistent Performance Gains. In E. Tzirita Zacharatou & A. Maté (Eds.), Proceedings of the 28th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026) co-located with the 29th International Conference on Extending Database Technology and the 28th International Conference on Database Theory (EDBT/ICDT 2026) : Tampere, Finland, March 24, 2026 (pp. 18–33). https://doi.org/10.34726/12619
    Download: Paper (1.26 MB)
    Project: DeConquer (2023–2027)
  • Database Theory in Action: Evaluation of Aggregate Queries Without Materialisation / Lanzinger, M., Pichler, R., & Selzer, A. (2026). Database Theory in Action: Evaluation of Aggregate Queries Without Materialisation. In 29th International Conference on Database Theory (ICDT 2026) (pp. 24:1-24:5). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICDT.2026.24
    Download: Paper (599 KB)
    Project: DeConquer (2023–2027)
  • Message Passing on the Edge: Towards Scalable and Expressive {GNN}s / Barcelo, P., Jogl, F., Kozachinskiy, A., Lanzinger, M. P., Neumann, S., & Rojas, C. (2026). Message Passing on the Edge: Towards Scalable and Expressive {GNN}s. In Forty-third International Conference on Machine Learning : ICML 2026. Forty-Third International Conference on Machine Learning (ICML 2026), Seoul, Korea (the Republic of). http://hdl.handle.net/20.500.12708/230075
    Projects: DeConquer (2023–2027) / VRG-TOSN (2023–2031)

2025

  • Soft and Constrained Hypertree Width / Lanzinger, M., Okulmus, C., Pichler, R., Selzer, A., & Gottlob, G. (2025). Soft and Constrained Hypertree Width. Proceedings of the ACM on Management of Data (PACMMOD), 3(2), 1–25. https://doi.org/10.1145/3725251
    Project: DeConquer (2023–2027)
  • Enabling Generalized Zero-Shot Vulnerability Classification / Hu, J., Guo, J., Luo, C., Hu, Y., Lanzinger, M., & Li, Z. (2025). Enabling Generalized Zero-Shot Vulnerability Classification. IEEE Transactions on Dependable and Secure Computing, 22(4), 3465–3482. https://doi.org/10.1109/TDSC.2025.3532487
  • Homomorphism Counts as Structural Encodings for Graph Learning / Bao, L., Jin, E., Bronstein, M. M., Ceylan, I. I., & Lanzinger, M. P. (2025). Homomorphism Counts as Structural Encodings for Graph Learning. In The Thirteenth International Conference on Learning Representations : ICLR 2025 (pp. 1–29). http://hdl.handle.net/20.500.12708/217250
    Project: DeConquer (2023–2027)
  • Avoiding Materialisation for Guarded Aggregate Queries / Lanzinger, M. P., Pichler, R., & Selzer, A. (2025). Avoiding Materialisation for Guarded Aggregate Queries. Proceedings of the VLDB Endowment, 18(5), 1398–1411. https://doi.org/10.14778/3718057.3718068
    Project: KnowledgeGraph (2020–2028)

2024

2023

  • Fractional covers of hypergraphs with bounded multi-intersection / Gottlob, G., Lanzinger, M., Pichler, R., & Razgon, I. (2023). Fractional covers of hypergraphs with bounded multi-intersection. Theoretical Computer Science, 979, Article 114204. https://doi.org/10.1016/j.tcs.2023.114204
    Download: Paper (460 KB)
    Projects: DeConquer (2023–2027) / HyperTrac (2018–2022)
  • Temporal Datalog with Existential Quantification / Lanzinger, M., Nissl, M., Sallinger, E., & Wałęga, P. (2023). Temporal Datalog with Existential Quantification. In E. Elkind (Ed.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023) (pp. 3277–3285). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/365

2022

  • MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations / Lanzinger, M., Sferrazza, S., & Gottlob, G. (2022). MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations. Theory and Practice of Logic Programming, 22(5), 678–692. https://doi.org/10.1017/S1471068422000199
    Project: HyperTrac (2018–2022)
  • New Perspectives for Fuzzy Datalog (Extended Abstract) / Lanzinger, M., Sferrazza, S., & Gottlob, G. (2022). New Perspectives for Fuzzy Datalog (Extended Abstract). In Proceedings of the 4th International Workshop on the Resurgence of Datalog in Academia and Industry (Datalog-2.0 2022) co-located with the 16th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR} 2022) (pp. 42–47). http://hdl.handle.net/20.500.12708/175762
    Download: PDF (1.17 MB)
    Project: HyperTrac (2018–2022)
  • Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth / Gottlob, G., Lanzinger, M., Okulmus, C., & Pichler, R. (2022). Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth. In Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (pp. 325–336). Association for Computing Machinery. https://doi.org/10.1145/3517804.3524153
    Project: HyperTrac (2018–2022)

2021

2020

2019

2017

  • Mehrwertige Logiken für Spiele / Lanzinger, M. P. (2017). Mehrwertige Logiken für Spiele [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2017.33948
    Download: PDF (781 KB)

2013