Frequent subgraph pattern mining on uncertain graph data

Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. The frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. An approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns. The algorithm uses an efficient approximation algorithm to determine whether a subgraph pattern can be output or not. The analytical and experimental results show that the algorithm is very efficient, accurate and scalable for large uncertain graph databases.