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Optimal assignment kernels for attributed molecular graphs

Published: 07 August 2005 Publication History

Abstract

We propose a new kernel function for attributed molecular graphs, which is based on the idea of computing an optimal assignment from the atoms of one molecule to those of another one, including information on neighborhood, membership to a certain structural element and other characteristics for each atom. As a byproduct this leads to a new class of kernel functions. We demonstrate how the necessary computations can be carried out efficiently. Compared to marginalized graph kernels our method in some cases leads to a significant reduction of the prediction error. Further improvement can be gained, if expert knowledge is combined with our method. We also investigate a reduced graph representation of molecules by collapsing certain structural elements, like e.g. rings, into a single node of the molecular graph.

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    cover image ACM Other conferences
    ICML '05: Proceedings of the 22nd international conference on Machine learning
    August 2005
    1113 pages
    ISBN:1595931805
    DOI:10.1145/1102351
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 August 2005

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    • (2024)State of the Art and Potentialities of Graph-level LearningACM Computing Surveys10.1145/369586357:2(1-40)Online publication date: 10-Oct-2024
    • (2024)HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338996636:11(6370-6384)Online publication date: Nov-2024
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