Attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.
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Graphs with labeled edges can always be transformed into graphs with only labels on nodes (using, e.g., the method proposed by ). For this reason, we only consider attributed nodes in our study.
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This work was supported by the ANR Grant “FOSTER” ANR-2010-COSI-012-01.
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Pasquier, C., Flouvat, F., Sanhes, J. et al. Attributed graph mining in the presence of automorphism. Knowl Inf Syst 50, 569–584 (2017). https://doi.org/10.1007/s10115-016-0953-9