Mining subgraph coverage patterns from graph transactions.

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the ubgraph D-based lat ransactional () framework for the efficient extraction of from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed framework is indeed capable of efficiently extracting from GTD. Furthermore, we demonstrate the effectiveness of through a case study in computer-aided drug design.