Efficient sampling methods for shortest path query over uncertain graphs

Graph has become a widely used structure to model data. Unfortunately, data are inherently with uncertainty because of the occurrence of noise and incompleteness in data collection. This is why uncertain graphs catch much attention of researchers. However, the uncertain graph models in existing works assume all edges in a graph are independent of each other, which dose not really make sense in real applications. Thus, we propose a new model for uncertain graphs considering the correlation among edges sharing the same vertex. Moreover, in this paper, we mainly solve the shortest path query, which is a funduemental but important query on graphs, using our new model. As the problem of calculating shortest path probability over correlated uncertain graphs is #P-hard, we propose different kinds of sampling methods to efficiently compute an approximate answer. The error is very small in our algorithm, which is proved and further verified in our experiments.