Fast Equiconcept Search on Graph Formal Context by Attribute Topology

The equiconcept, as an emerging topic in formal concept analysis, provides new ideas for analyzing the behavior in social computing. However, it takes too much time for calculation because of the high complexity. In this article, a fast equiconcept search method on graph formal context by attribute topology is proposed to improve computational efficiency. First, attribute topology is optimally represented as a graph context attribute topology (GcAT) model on the graph formal context. Then, it is proved that there is a one-to-one match between the maximal complete polygon in GcAT and the equiconcept in the graph formal context. Finally, combined with the existing attribute topology formal concept searching algorithm, the fast equiconcept search is completed. The experimental results show that by the proposed method not only all equiconcepts are searched, but also the time consumption is significantly reduced compared with the other methods over seven datasets. In addition, with the size of the dataset increasing, the computational efficiency of GcAT model improves more obviously, which indicates its superiority. All of that demonstrate the proposed method has greatly improved the efficiency of equiconcept search through GcAT on graph formal context.