WebKernel Hilbert Spaces, and used these extensions to define a unifying framework for random walk kernels. They showed that computing many random walk graph ker- ... random walk and marginalized ... WebA family of efficient kernels for large graphs with discrete node labels based on the Weisfeiler-Lehman test of isomorphism on graphs that outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Expand
arXiv:0807.0093v1 [cs.LG] 1 Jul 2008
WebExtensions of marginalized graph kernels, in: Proc. the twenty-first international conference on Machine learning, ACM. p. 70. [8] Lifan Xu, Wei Wang, M Alvarez, John Cavazos, and Dongping Zhang. Parallelization of shortest path graph kernels on multi-core cpus and gpus. Proceedings of the Programmability Issues for Heterogeneous Multicores ... WebThe term graph kernel is used in two related but distinct contexts: On the one hand, graph kernels can be defined between graphs, that is, as a kernel function k : \mathcal {G}\,\times \,\mathcal {G}\rightarrow \mathbb {R} where \mathcal {G} denotes the set of all graphs un-der consideration. In the most common setting \mathcal {G} is the set ... icaew accountants list
Entity disambiguation in anonymized graphs using graph kernels
WebDec 4, 2024 · Resampling techniques can address this issue but these procedures are time-consuming. This problem is particularly challenging when dealing with structured data, in particular with graphs, since several kernels for graph data have been proposed in literature, but no clear relationship among them in terms of learning properties is defined. WebJan 15, 2016 · Graphs are flexible and powerful representations for non-vectorial structured data. Graph kernels have been shown to enable efficient and accurate statistical learning on this important domain, but many graph kernel algorithms have high order polynomial time complexity. Efficient graph kernels rely on a discrete node labeling as a central ... WebGraph kernels have been successfully applied on chemical graphs on small to medium sized machine learning problems. However, graph kernels often require a graph transformation before the computation can be applied. ... P., Ueda, N., Akutsu, T., Perret, J.-L., Vert, J.-P.: Extensions of marginalized graph kernels. In: ICML 2004: Proceedings … mondo insieme work and travel