… subgraph mining methods. On this basis, we propose the Labeled Subgraph Entropy Kernel
(… Our subgraph entropy kernel is performed in multiple types of application scenarios, such …

Graph kernels are applied heavily for the classification of structured data. In this paper, we
propose a deep Rényi entropy graph kernel for this purpose. We gauge the deep information …

… -based graph kernel and the all subgraph kernel and thus … undirected graphs is a canonical
labeling method. The key idea of … on using the kernel methods and entropy based complexity …

… kernel based on the concept of entropy. Our method has the advantage of handling labeled
… The entropy that we define for the subgraph \(\zeta _v^k\) with respect to the root v is then …

… where a sub-graph is not connected to any labeled vertex. We conjecture that by maximizing
the entropy of each pi, the classifier has a better chance of producing high entropy results in …

… kernel which is related to the maximum common subgraph … from a maximum common
subgraph our approach counts the … to induced subgraphs and assume graphs to be labeled or …

… kernel. Thirdly, based on the two aforementioned forms of … entropy and H 1 -reproducing
kernel, we define two reproducing graph kernels: one is approximated von Neumann entropy …

… ; the four below are marked with negative labels and belong … novel subgraph-mining technique
Entropy-based Subgraph … -SVM is the penalty factor of Gaussian kernel (Cost), which is …

… The support vector machine classifier with a radial basis function kernel and node entropy
features performs better for classifying two states with highest accuracy, specificity and …

… -Distance Neighborhood-Subgraph Kernel based on decomposing … graph kernels. To this
end, we propose a novel quantum kernel, namely the Quantum-based Entropic Subtree Kernel …