no code implementations • 12 Mar 2024 • Harish G. Naik, Jan Polster, Raj Shekhar, Tamás Horváth, György Turán
EEGL is an iterative self-improving algorithm, which starts with a learned "vanilla" GNN, and repeatedly uses frequent subgraph mining to find relevant patterns in explanation subgraphs.
no code implementations • 10 May 2021 • Eike Stadtländer, Tamás Horváth, Stefan Wrobel
Although it has been shown quite a while ago that efficient learning of weakly convex hypotheses, a parameterized relaxation of convex hypotheses, is possible for the special case of Boolean functions, the question of whether this idea can be developed into a generic paradigm has not been studied yet.
no code implementations • 20 Jan 2021 • Till Hendrik Schulz, Tamás Horváth, Pascal Welke, Stefan Wrobel
The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance.