Abstract
This paper contributes to the problem of assessing similarities between node-labeled and edge-weighted graphs. Graph comparison is usually based on the maximum common subgraph (mcs) measure. The latter is an overly stringent measure which is sensitive toward small deviations and errors. In order to overcome these issues, we propose a relaxation of the mcs measure based on so-called communities. A community is used as an “almost common” subgraph with high concentrations of edges. With our approach, we increase tolerance towards noise and structural variation especially in the case of biological data. The proposed measure is validated by an experimental study conducted in the context of the analysis of the similarities among protein families based on the properties of their active sites.
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Mallek, S., Boukhris, I., Elouedi, Z. (2015). Predicting Proteins Functional Family: A Graph-Based Similarity Derived from Community Detection. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_54
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DOI: https://doi.org/10.1007/978-3-319-11310-4_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
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