Palme, Richard and Welke, Pascal (2023) Frequent Generalized Subgraph Mining via Graph Edit Distances. In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022) :. Communications in Computer and Information Science . Springer, Cham, pp. 477-483. ISBN 9783031236327
Full text not available from this repository.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.