@inproceedings{ca7f01340ebf48af8dd83b963dd420da,
title = "A graph kernel from the depth-based representation",
abstract = "In this paper we develop a novel graph kernel by matching the depth-based substructures in graphs. We commence by describing how to compute the Shannon entropy of a graph using random walks. We then develop an h-layer depth-based representations for a graph, which is effected by measuring the Shannon entropies of a family of K-layer expansion subgraphs derived from a vertex of the graph. The depth-based representations characterize graphs in terms of high dimensional depth-based complexity information. Based on the new representation, we establish a possible correspondence between vertices of two graphs that allows us to construct a matching-based graph kernel. Experiments on graphs from computer vision datasets demonstrate the effectiveness of our kernel.",
keywords = "Depth-based representation, graph kernels, graph matching",
author = "Lu Bai and Peng Ren and Xiao Bai and Hancock, \{Edwin R.\}",
year = "2014",
doi = "10.1007/978-3-662-44415-3\_1",
language = "英语",
isbn = "9783662444146",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--11",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Proceedings",
address = "德国",
note = "Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014 ; Conference date: 20-08-2014 Through 22-08-2014",
}