Clones in Graphs
Abstract
Finding structural similarities in graph data, like social networks, is a farranging task in data mining and knowledge discovery. A (conceptually) simple reduction would be to compute the automorphism group of a graph. However, this approach is ineffective in data mining since real world data does not exhibit enough structural regularity. Here we step in with a novel approach based on mappings that preserve the maximal cliques. For this we exploit the well known correspondence between bipartite graphs and the data structure formal context $(G,M,I)$ from Formal Concept Analysis. From there we utilize the notion of clone items. The investigation of these is still an open problem to which we add new insights with this work. Furthermore, we produce a substantial experimental investigation of real world data. We conclude with demonstrating the generalization of clone items to permutations.
 Publication:

arXiv eprints
 Pub Date:
 February 2018
 arXiv:
 arXiv:1802.07849
 Bibcode:
 2018arXiv180207849D
 Keywords:

 Computer Science  Social and Information Networks;
 03G10 91D30;
 G.2.1;
 G.2.2
 EPrint:
 11 pages, 2 figures, 1 table