@inproceedings{cae3440315cb4a719da78ea7f3252f1c,

title = "Attributed graph kernels using the Jensen-Tsallis q-differences",

abstract = "We propose a family of attributed graph kernels based on mutual information measures, i.e., the Jensen-Tsallis (JT) q-differences (for q ∈ [1,2]) between probability distributions over the graphs. To this end, we first assign a probability to each vertex of the graph through a continuous-time quantum walk (CTQW). We then adopt the tree-index approach [1] to strengthen the original vertex labels, and we show how the CTQW can induce a probability distribution over these strengthened labels. We show that our JT kernel (for q = 1) overcomes the shortcoming of discarding non-isomorphic substructures arising in the R-convolution kernels. Moreover, we prove that the proposed JT kernels generalize the Jensen-Shannon graph kernel [2] (for q = 1) and the classical subtree kernel [3] (for q = 2), respectively. Experimental evaluations demonstrate the effectiveness and efficiency of the JT kernels.",

keywords = "continuous-time quantum walk, Graph kernels, Jensen-Tsallis q-differences, tree-index method",

author = "Lu Bai and Luca Rossi and Horst Bunke and Hancock, {Edwin R.}",

year = "2014",

month = dec,

day = "31",

doi = "10.1007/978-3-662-44848-9_7",

language = "English",

isbn = "978-3-662-44847-2",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "99--114",

editor = "Toon Calders and Floriana Esposito and Eyke H{\"u}llermeier and Rosa Meo",

booktitle = "Machine Learning and Knowledge Discovery in Databases",

address = "Germany",

note = "European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 ; Conference date: 15-09-2014 Through 19-09-2014",

}