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. © 2014 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bai, L., Ren, P., Bai, X., & Hancock, E. R. (2014). A graph kernel from the depth-based representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8621 LNCS, pp. 1–11). Springer Verlag. https://doi.org/10.1007/978-3-662-44415-3_1
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