Structural-semantic approach for approximate frequent subgraph mining

Frequent subgraph mining refers usually to graph matching and it is widely used when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structural semantic graph matching to discover a set of frequent subgraphs. It uses both similarity measures. An approximate structural similarity function based on graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function. Both structural and semantic filters contribute together to prune extracted frequent sets. Indeed, new hybrid structural-semantic frequent subgraph mining approach will be suitable to be applied to several applications such as community detection and social network analysis.