Affiliations: [a] Department of Engineering, University of Ferrara, Ferrara, Italy | [b] National Institute of Informatics, Tokyo, Japan | [c] JST, PRESTO
Correspondence:
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Corresponding author: Elena Bellodi, Via Saragat 1 – Blocco A, Dipartimento di Ingegneria, 44122 Ferrara, Italy. Tel.: +39 0532974882; E-mail: [email protected].
Abstract: Although recent advances of significant subgraph mining enable us to find subgraphs that are statistically significantly associated with the class variable from graph databases, it is challenging to interpret the resulting subgraphs due to their massive number and their propositional representation. Here we represent graphs by probabilistic logic programming and solve the problem of summarizing significant subgraphs by structure learning of probabilistic logic programs. Learning probabilistic logical models leads to a much more interpretable, expressive and succinct representation of significant subgraphs. We empirically demonstrate that our approach can effectively summarize significant subgraphs with keeping high accuracy.
