Frequent Generalized Subgraph Mining via Graph Edit Distances
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.
- Published in:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022 - Type:
Inproceedings - Authors:
Palme, Richard; Welke, Pascal - Year:
2022 - Source:
https://link.springer.com/chapter/10.1007/978-3-031-23633-4_32
Citation information
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, 2022, https://link.springer.com/chapter/10.1007/978-3-031-23633-4_32, Palme.Welke.2022a,
@Inproceedings{Palme.Welke.2022a,
author={Palme, Richard; Welke, Pascal},
title={Frequent Generalized Subgraph Mining via Graph Edit Distances},
booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022},
url={https://link.springer.com/chapter/10.1007/978-3-031-23633-4_32},
year={2022},
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...}}
