By the same authors

A graph kernel from the depth-based representation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DatePublished - 1 Jan 2014
Pages1-11
Number of pages11
PublisherSpringer-Verlag
EditorsP Franti, G Brown, M Loog, F Escolano, M Pelillo
Volume8621
Original languageEnglish
ISBN (Electronic)978-3-662-44415-3
ISBN (Print)9783662444146

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8621 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

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.

    Research areas

  • Depth-based representation, graph kernels, graph matching

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