Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision | Previous revision | ||
staff:kriege [2019-09-09 14:26] |
staff:kriege [2019-10-03 13:32] |
||
---|---|---|---|
Line 109: | Line 109: | ||
* **A Survey on Graph Kernels** (Preprint [[https://arxiv.org/abs/1903.11835|arXiv:1903.11835]]) \\ Nils M. Kriege, Fredrik D. Johansson, Christopher Morris \\ Applied Network Science, Machine learning with graphs, 2019, accepted for publication. | * **A Survey on Graph Kernels** (Preprint [[https://arxiv.org/abs/1903.11835|arXiv:1903.11835]]) \\ Nils M. Kriege, Fredrik D. Johansson, Christopher Morris \\ Applied Network Science, Machine learning with graphs, 2019, accepted for publication. | ||
- | * **A unifying view of explicit and implicit feature maps of graph kernels** (Preprint [[https://arxiv.org/abs/1703.00676|arXiv:1703.00676]]) \\ Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel \\ Data Mining and Knowledge Discovery, 2019, accepted for publication. | + | * **[[https://doi.org/10.1007/s10618-019-00652-0|A unifying view of explicit and implicit feature maps of graph kernels]]** (Preprint [[https://arxiv.org/abs/1703.00676|arXiv:1703.00676]]) \\ Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel \\ Data Mining and Knowledge Discovery, 2019, accepted for publication. |
* **[[https://doi.org/10.1007/s00453-019-00552-1|A general purpose algorithm for counting simple cycles and simple paths of any length]]** (Preprint [[https://arxiv.org/abs/1612.05531|arXiv:1612.05531]]) \\ Pierre-Louis Giscard, Nils Kriege, Richard C. Wilson \\ Algorithmica, vol. 81, no. 7, 2716-2737, 2019. | * **[[https://doi.org/10.1007/s00453-019-00552-1|A general purpose algorithm for counting simple cycles and simple paths of any length]]** (Preprint [[https://arxiv.org/abs/1612.05531|arXiv:1612.05531]]) \\ Pierre-Louis Giscard, Nils Kriege, Richard C. Wilson \\ Algorithmica, vol. 81, no. 7, 2716-2737, 2019. | ||
Line 126: | Line 126: | ||
* **Computing Optimal Assignments in Linear Time for Approximate Graph Matching** (Preprint [[https://arxiv.org/abs/1901.10356|arXiv:1901.10356]]) \\ Nils M. Kriege, Pierre-Louis Giscard, Franka Bause, Richard C. Wilson \\ International Conference on Data Mining (ICDM) 2019, accepted for publication. | * **Computing Optimal Assignments in Linear Time for Approximate Graph Matching** (Preprint [[https://arxiv.org/abs/1901.10356|arXiv:1901.10356]]) \\ Nils M. Kriege, Pierre-Louis Giscard, Franka Bause, Richard C. Wilson \\ International Conference on Data Mining (ICDM) 2019, accepted for publication. | ||
- | * **Protein Complex Similarity based on Weisfeiler-Lehman labeling** ([[https://doi.org/10.7287/peerj.preprints.26612v1|PeerJ Preprints, vol. 6, e26612v1]]) \\ Bianca K. Stöcker, Till Schäfer, Petra Mutzel, Johannes Köster, Nils Kriege, Sven Rahmann \\ International Conference on Similarity Search and Applications (SISAP) 2019, accepted for publication. | + | * **[[https://doi.org/10.1007/978-3-030-32047-8_27|Protein Complex Similarity based on Weisfeiler-Lehman labeling]]** ([[https://doi.org/10.7287/peerj.preprints.26612v1|PeerJ Preprints, vol. 6, e26612v1]]) \\ Bianca K. Stöcker, Till Schäfer, Petra Mutzel, Johannes Köster, Nils Kriege, Sven Rahmann \\ International Conference on Similarity Search and Applications (SISAP) 2019, 308-322. |
* **[[https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-60.pdf|Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning]]** (Preprint [[https://arxiv.org/abs/1908.06661|arXiv:1908.06661]]) \\ Nils M. Kriege \\ European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2019. | * **[[https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-60.pdf|Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning]]** (Preprint [[https://arxiv.org/abs/1908.06661|arXiv:1908.06661]]) \\ Nils M. Kriege \\ European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2019. |