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Publikationen:
Bücher:

• Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber (eds., 2021):
  Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings, Springer.
• Tanya Braun, Marcel Gehrke, Tom Hanika, Nathalie Hernandez (eds., 2021):
  Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Proceedings, Springer.

• Haya Alyoussef, Ahmad Bdeir, Diego Coello de Portugal Mecke, Tom Hanika, Niels Landwehr, Lars Schmidt-Thieme (2026):
  HexFormer: Hyperbolic Vision Transformer with Exponential Map Aggregation, in Arxiv.
• Diego Coello de Portugal Mecke, Haya Alyoussef, Maximilian Stubbemann, Ilia Koloiarov, Tom Hanika, Lars Schmidt-Thieme (2025):
  STADE: Standard Deviation as a Pruning Metric , in Arxiv.
• Tobias Hille, Maximilian Stubbemann, Tom Hanika (2024):
  Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, Transactions on Machine Learning Research .
• Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika (2024):
  Ordinal motifs in lattices, Information Sciences :120009.
• Katharina B. Budde, Christian Rellstab, Myriam Heuertz, Felix Gugerli, Tom Hanika, Miguel Verdú, Juli G. Pausas, Santiago C. González-Martínez (2024):
  Divergent selection in a Mediterranean pine on local spatial scales, Journal of Ecology 2:278-290.
• Tom Hanika, Tobias Hille (2024):
  What is the Intrinsic Dimension of Your Binary Data? - and How to Compute it Quickly, in Conceptual Knowledge Structures - First International Joint Conference, CONCEPTS 2024, Proceedings, Springer, pp. 97-112.
• Tom Hanika, Robert Jäschke (2024):
  A Repository for Formal Contexts, in Conceptual Knowledge Structures - First International Joint Conference, CONCEPTS 2024, Proceedings, Springer, pp. 182-197.
• Bernhard Ganter, Tom Hanika, Johannes Hirth, Sergei Obiedkov (2024):
  Collaborative Hybrid Human AI Learning through Conceptual Exploration, in Proceedings of the Workshops at the Third International Conference on Hybrid Human-Artificial Intelligence (HHAI 2024), CEUR-WS.org, pp. 1-8.
• Maximilian Stubbemann, Tobias Hille, Tom Hanika (2023):
  Selecting Features by their Resilience to the Curse of Dimensionality, in arXiv:2304.02455.
• Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider (2023):
  Intrinsic Dimension for Large-Scale Geometric Learning, Transactions on Machine Learning Research .
• Dominik Duerrschnabel, Maximilian Stubbemann, Tom Hanika (2022):
  FCA2VEC: Embedding Techniques for Formal Concept Analysis, in Complex Data Analytics with Formal Concept Analysis.
• Tom Hanika, Friedrich Martin Schneider, Gerd Stumme (2022):
  Intrinsic dimension of geometric data sets, Tohoku Mathematical Journal 1:23-52.
• Tom Hanika, Johannes Hirth (2022):
  Knowledge Cores in Large Formal Contexts, Annals of Mathematics and Artificial Intelligence 6:537-567.
• Tom Hanika, Johannes Hirth (2022):
  On the lattice of conceptual measurements, Information Sciences :453-468.
• Tobias Koopmann, Maximilian Stubbemann, Matthias Kapa, Michael Paris, Guido Buenstorf, Tom Hanika, Andreas Hotho, Robert Jaeschke, Gerd Stumme (2021):
  Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research, Scientometrics :9847-9868.
• Bastian Schaefermeier, Gerd Stumme, Tom Hanika (2021):
  Topic space trajectories, Scientometrics 7:5759-5795.
• Tom Hanika, Johannes Hirth (2021):
  Exploring Scale-Measures of Data Sets, in 16th ICFCA, Proceedings, Springer, pp. 261-269.
• Tom Hanika, Johannes Hirth (2021):
  Quantifying the Conceptual Error in Dimensionality Reduction, in Graph-Based Representation and Reasoning - 26th ICCS 2021, Springer, pp. 105-118.
• Maximilian Stubbemann, Tom Hanika, Gerd Stumme (2020):
  Orometric Methods in Bounded Metric Data, in Advances in Intelligent Data Analysis XVIII.
• Daniel Borchmann, Tom Hanika, Sergei Obiedkov (2020):
  Probably approximately correct learning of Horn envelopes from queries, Discrete Applied Mathematics :30-42.
• Tom Hanika, Maximilian Marx, Gerd Stumme (2019):
  Discovering Implicational Knowledge in Wikidata, in Formal Concept Analysis - 15th International Conference, ICFCA 2019, Proceedings, Springer, pp. 315-323.
• Maximilian Felde, Tom Hanika (2019):
  Formal Context Generation Using Dirichlet Distributions, in 24th International Conference on Conceptual Structures, ICCS 2019, Proceedings, Springer, pp. 57-71.
• Tom Hanika, Maren Koyda, Gerd Stumme (2019):
  Relevant Attributes in Formal Contexts, in Graph-Based Representation and Reasoning, ICCS 2019, Proceedings, Springer, pp. 102-116.
• Dominik Dürrschnabel, Tom Hanika, Gerd Stumme (2019):
  DimDraw - A Novel Tool for Drawing Concept Lattices, in Supplementary Proceedings of ICFCA 2019 Conference, CEUR-WS.org, pp. 60-64.
• Bastian Schaefermeier, Tom Hanika, Gerd Stumme (2019):
  Distances for wifi based topological indoor mapping, in Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ACM.
• Tom Hanika, Mark Kibanov, Jonathan Kropf, Stefan Laser (2019):
  Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt populär und besonders interessant für die Forschung geworden ist., in Digitale Bewertungspraktiken, Springer, pp. 165-188.
• Tom Hanika, Johannes Hirth (2019):
  Conexp-Clj - A Research Tool for FCA, in ICFCA (Supplementary), CEUR-WS.org, pp. 70-75.
• Stephan Doerfel, Tom Hanika, Gerd Stumme (2018):
  Clones in Graphs, in Foundations of Intelligent Systems - 24th International Symposium, ISMIS 2018, Springer, pp. 56-66.
• Tom Hanika, Jens Zumbrägel (2018):
  Towards Collaborative Conceptual Exploration, in Graph-Based Representation and Reasoning, ICCS 2018, Proceedings, Springer, pp. 120-134.
• Daniel Borchmann, Tom Hanika (2017):
  Individuality in Social Networks, in Formal Concept Analysis of Social Networks, Springer, pp. 19-40.
• Daniel Borchmann, Tom Hanika, Sergei Obiedkov (2017):
  On the Usability of Probably Approximately Correct Implication Bases, in Formal Concept Analysis - 14th International Conference, ICFCA 2017, Proceedings, Springer, pp. 72-88.
• Martin Atzmueller, Tom Hanika, Gerd Stumme, Richard Schaller, Bernd Ludwig (2016):
  Social event network analysis: Structure, preferences, and reality, in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, pp. 613-620.
• Daniel Borchmann, Tom Hanika (2016):
  Some Experimental Results on Randomly Generating Formal Contexts, in 13th International Conference on Concept Lattices and Their Applications, CEUR-WS.org, pp. 57-69.