Abstract
problems are still not solved and need more attention from researchers such as inexact subgraph matching when similarity among subgraphs is not totally proved. Furthermore, many research works dealt with structural graph matching, but a little attention was paid to semantic graph matching when graph vertices and/or edges are attributed and typed. Therefore, this paper proposes a new possibilistic flexible graph mining approach to discover similar subgraphs, by applying possibilistic similarity rather than using hard structural exact similarity. This novel approach is hybrid and uses two similarity measures. An approximate structural similarity function of graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function. Experimental evaluation was conducted on real datasets borrowed from various domains and tests have shown a highly superiority of our hybrid frequent subgraph mining approach by achieving a good time processing performance and simultaneously discovering more similar subgraphs. (C) 2018 The Authors. Published by Elseiver Ltd.