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Nowadays, frequent graph mining approaches are very popular in several applications and big data domains. However, some 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...
Frequent subgraph mining is useful in most knowledge discovery tasks such as classification, clustering and indexing. Many algorithms and methods have been developed to mine frequent subgraphs. To have an understanding of several mining frequent subgraph algorithms, it is advantageous to establish a common framework for their study. In this paper, we propose a comparative study of several approaches...
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...
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