Graph Kernels: State-of-the-Art and Future Challenges. - Karsten M. Borgwardt; M. Elisabetta Ghisu; Felipe Llinares-López; Leslie O'Bray; Bastian Rieck (Found. Trends Mach. Learn., 2020)

Filtration Curves for Graph Representation. - Leslie O'Bray; Bastian Rieck; Karsten M. Borgwardt (KDD, 2021)

Biological network analysis with deep learning. - Giulia Muzio; Leslie O'Bray; Karsten M. Borgwardt (Briefings Bioinform., 2021)

Towards a Taxonomy of Graph Learning Datasets. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2021)

The magnitude vector of images. - Michael F. Adamer; Leslie O'Bray; Edward De Brouwer; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2021)

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. - Leslie O'Bray; Max Horn; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Structure-Aware Transformer for Graph Representation Learning. - Dexiong Chen; Leslie O'Bray; Karsten M. Borgwardt (ICML, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Sarah McGuire; Xinyi Wang; Anna Little; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; Ladislav Rampásek (LoG, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2022)

Leveraging global information for machine learning on graphs. - Leslie O'Bray (2023)

The magnitude vector of images. - Michael F. Adamer; Edward De Brouwer; Leslie O'Bray; Bastian Rieck (J. Appl. Comput. Topol., 2024)