Analysing Neural Network Topologies: a Game Theoretic Approach. - Julian Stier; Gabriele Gianini; Michael Granitzer; Konstantin Ziegler (KES, 2018)

Structural Analysis of Sparse Neural Networks. - Julian Stier; Michael Granitzer (KES, 2019)

Deep Graph Generators. - Julian Stier; Michael Granitzer (CoRR, 2020)

Evofficient: Reproducing a Cartesian Genetic Programming Method. - Lorenz Wendlinger; Julian Stier; Michael Granitzer (EuroGP, 2021)

DeepGG: A Deep Graph Generator. - Julian Stier; Michael Granitzer (IDA, 2021)

Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors. - Mehdi Ben Amor; Julian Stier; Michael Granitzer (KES, 2021)

Experiments on Properties of Hidden Structures of Sparse Neural Networks. - Julian Stier; Harshil Darji; Michael Granitzer (LOD, 2021)

Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. - Clemens Havas; Lorenz Wendlinger; Julian Stier; Sahib Julka; Veronika Krieger; Cornelia Ferner; Andreas Petutschnig; Michael Granitzer; Stefan Wegenkittl; Bernd Resch (ISPRS Int. J. Geo Inf., 2021)

deepstruct - linking deep learning and graph theory. - Julian Stier; Michael Granitzer (Softw. Impacts, 2022)

GRAN Is Superior to GraphRNN: Node Orderings, Kernel- and Graph Embeddings-Based Metrics for Graph Generators. - Ousmane Touat; Julian Stier; Pierre-Edouard Portier; Michael Granitzer (LOD (1), 2023)

Efficient NAS with FaDE on Hierarchical Spaces. - Simon Neumeyer; Julian Stier; Michael Granitzer (IDA (2), 2024)

MCGRAN: Multi-conditional Graph Generation for Neural Architecture Search. - Sathish Purushothaman; Julian Stier; Michael Granitzer (LOD (1), 2024)

Structure of Artificial Neural Networks - Empirical Investigations. - Julian Stier (CoRR, 2024)

Structure of Artificial Neural Networks: Empirical Investigations. - Julian Stier (2024)