1 code implementation • 23 Mar 2024 • Daniel Faber, Adalat Jabrayilov, Petra Mutzel
For the widely studied GCP, we experimentally compare our new SAT encoding to the state-of-the-art approaches on the DIMACS benchmark set.
1 code implementation • 12 Sep 2022 • Lutz Oettershagen, Nils M. Kriege, Claude Jordan, Petra Mutzel
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs.
1 code implementation • 8 Feb 2022 • Lutz Oettershagen, Petra Mutzel, Nils M. Kriege
We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network.
2 code implementations • 16 Jul 2020 • Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann
We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
no code implementations • 14 Oct 2019 • Lutz Oettershagen, Nils M. Kriege, Christopher Morris, Petra Mutzel
Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.
1 code implementation • NeurIPS 2020 • Christopher Morris, Gaurav Rattan, Petra Mutzel
Hence, it accounts for the higher-order interactions between vertices.
Ranked #3 on Graph Classification on NCI109
no code implementations • 16 Feb 2018 • Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert
To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size.
1 code implementation • 7 Mar 2017 • Christopher Morris, Kristian Kersting, Petra Mutzel
Specifically, we introduce a novel graph kernel based on the $k$-dimensional Weisfeiler-Lehman algorithm.
no code implementations • 2 Mar 2017 • Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel
On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.
no code implementations • 1 Oct 2016 • Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.
no code implementations • 28 Sep 2016 • Till Schäfer, Petra Mutzel
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy.
no code implementations • 27 Jun 2012 • Nils Kriege, Petra Mutzel
To compute the kernel we propose a graph-theoretical algorithm inspired by a classical relation between common subgraphs of two graphs and cliques in their product graph observed by Levi (1973).