Significant Subgraph Mining with Multiple Testing Correction

no code implementations • 1 Jul 2014 • Mahito Sugiyama, Felipe Llinares López, Niklas Kasenburg, Karsten M. Borgwardt

An open question, however, is whether this strategy of excluding untestable hypotheses also leads to greater statistical power in subgraph mining, in which the number of hypotheses is much larger than in itemset mining.

Open-Ended Question Answering Two-sample testing

Scalable kernels for graphs with continuous attributes

no code implementations • NeurIPS 2013 • Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.

General Classification