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:
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
  • Type:
    Inproceedings
  • Authors:
    Palme, Richard; Welke, Pascal
  • Year:
    2022

Citation information

Palme, Richard; Welke, Pascal: Frequent Generalized Subgraph Mining via Graph Edit Distances, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022, https://link.springer.com/chapter/10.1007/978-3-031-23633-4_32, Palme.Welke.2022a,

Associated Lamarr Researchers

lamarr institute person Welke Pascal - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Pascal Welke

Autor to the profile