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Frequent Generalized Subgraph Mining via Graph Edit Distances

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Frequent Generalized Subgraph Mining via Graph Edit Distances. / Palme, Richard; Welke, Pascal.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). ed. / Irena Koprinska. Cham: Springer, 2023. p. 477-483 (Communications in Computer and Information Science; Vol. 1753).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Palme, R & Welke, P 2023, Frequent Generalized Subgraph Mining via Graph Edit Distances. in I Koprinska (ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Communications in Computer and Information Science, vol. 1753, Springer, Cham, pp. 477-483. https://doi.org/10.1007/978-3-031-23633-4_32

APA

Palme, R., & Welke, P. (2023). Frequent Generalized Subgraph Mining via Graph Edit Distances. In I. Koprinska (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022) (pp. 477-483). (Communications in Computer and Information Science; Vol. 1753). Springer. https://doi.org/10.1007/978-3-031-23633-4_32

Vancouver

Palme R, Welke P. Frequent Generalized Subgraph Mining via Graph Edit Distances. In Koprinska I, editor, Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Cham: Springer. 2023. p. 477-483. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-23633-4_32

Author

Palme, Richard ; Welke, Pascal. / Frequent Generalized Subgraph Mining via Graph Edit Distances. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). editor / Irena Koprinska. Cham : Springer, 2023. pp. 477-483 (Communications in Computer and Information Science).

Bibtex

@inproceedings{cbf66c4e30234e1ab3f6ba61e9ea5966,
title = "Frequent Generalized Subgraph Mining via Graph Edit Distances",
abstract = "In this work, we propose a method for computing generalized frequent subgraph patterns which is based on the graph edit distance. Graph data is often equipped with semantic information in form of an ontology, for example when dealing with linked data or knowledge graphs. Previous work suggests to exploit this semantic information in order to compute frequent generalized patterns, i.e. patterns for which the total frequency of all more specific patterns exceeds the frequency threshold. However, the problem of computing the frequency of a generalized pattern has not yet been fully addressed.",
author = "Richard Palme and Pascal Welke",
year = "2023",
month = jan,
day = "31",
doi = "10.1007/978-3-031-23633-4_32",
language = "English",
isbn = "9783031236327",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "477--483",
editor = "Irena Koprinska",
booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)",

}

RIS

TY - GEN

T1 - Frequent Generalized Subgraph Mining via Graph Edit Distances

AU - Palme, Richard

AU - Welke, Pascal

PY - 2023/1/31

Y1 - 2023/1/31

N2 - In this work, we propose a method for computing generalized frequent subgraph patterns which is based on the graph edit distance. Graph data is often equipped with semantic information in form of an ontology, for example when dealing with linked data or knowledge graphs. Previous work suggests to exploit this semantic information in order to compute frequent generalized patterns, i.e. patterns for which the total frequency of all more specific patterns exceeds the frequency threshold. However, the problem of computing the frequency of a generalized pattern has not yet been fully addressed.

AB - In this work, we propose a method for computing generalized frequent subgraph patterns which is based on the graph edit distance. Graph data is often equipped with semantic information in form of an ontology, for example when dealing with linked data or knowledge graphs. Previous work suggests to exploit this semantic information in order to compute frequent generalized patterns, i.e. patterns for which the total frequency of all more specific patterns exceeds the frequency threshold. However, the problem of computing the frequency of a generalized pattern has not yet been fully addressed.

U2 - 10.1007/978-3-031-23633-4_32

DO - 10.1007/978-3-031-23633-4_32

M3 - Conference contribution/Paper

SN - 9783031236327

T3 - Communications in Computer and Information Science

SP - 477

EP - 483

BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

A2 - Koprinska, Irena

PB - Springer

CY - Cham

ER -