Superdiffusive quantum stochastic walk definable on arbitrary directed graph

In this paper we define a quantum stochastic walk on arbitrary directed graph with super-diffusive propagation on a line graph. Our model is based on global environment interaction QSW, which is known to have ballistic propagation. However we discovered, that in this case additional amplitude transitions occur, hence graph topology is changed into moral graph. Because of that we call the effect a spontaneous moralization. We propose a general correction scheme, which is proved to remove unnecessary transition and thus to preserve the graph topology. In the end we numerically show, that superdiffusive propagation is preserved. Because of that our new model may be applied as effective evolution on arbitrary directed graph.

A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

Learning Bayesian network (BN) structure from data is a typical NP-hard problem. But almost existing algorithms have the very high complexity when the number of variables is large. In order to solve this problem(s), we present an algorithm that integrates with a decomposition-based approach and a scoring-function-based approach for learning BN structures. Firstly, the proposed algorithm decomposes the moral graph of BN into its maximal prime subgraphs. Then it orientates the local edges in each subgraph by the K2-scoring greedy searching. The last step is combining directed subgraphs to obtain final BN structure. The theoretical and experimental results show that our algorithm can efficiently and accurately identify complex network structures from small data set.