Extended Graph Backbone for Motif Analysis

Local interaction patterns in complex networks, called motifs, explain many of the network properties but are challenging to extract due to the large search space. In this paper first an approximate representation of a complex network in terms of an extended backbone is proposed, then a reduced sampling space that speeds up the motif search in different kinds of networks is explored based on this representation. It will be shown using several real datasets that the proposed method is effective in reducing the sampling space, extracts the same relevant patterns, and hence preservs the network local structural information.

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