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Directed networks find many applications in computer science, social science and biomedicine, among others. In this paper we propose a new graph mining algorithm that is capable of locating all frequent induced subgraphs in a given set of directed networks. We present an incremental coding scheme for representing the canonical form of a graph, study its properties, and develop new techniques for pattern generation suitable for directed networks. We prove that our algorithm is complete, meaning that no qualified pattern is missed by the algorithm. Furthermore, our algorithm is correct in the sense that all patterns found by the algorithm are frequent induced subgraphs in the given networks. Experimental results based on synthetic data and gene regulatory networks show the good performance of our algorithm, and its application in network inference. [ABSTRACT FROM AUTHOR]

This paper explores the idea of closed world specialisation (CWS). While traditional CWS is performed as a postprocessing step, we propose two different approaches to incorporating it into the induction process of a bottom-up inductive logic programming system. The motivation comes from the fact that using CWS as a postprocessing step is incapable of solving problems in which the negated part of the hypothesis is crucial. We apply the proposed approaches to the ProGolem bottom-up ILP system. We give examples of problems, where classical CWS fails to find a complete and consistent solution, whereas the proposed approaches succeed. Tests on real-world datasets show that the proposed approaches perform at least as well as regular CWS, while being better in terms of predictive accuracy in some cases. We also point out some weaknesses of different CWS approaches. [ABSTRACT FROM AUTHOR]