UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases

Large graph data repositories are becoming in-creasingly common. Identifying frequently appearing subgraph patterns in such databases can reveal useful information, and such patterns have been used for a variety of applications. Im-perfections and stochasticity are often unavoidable in real-world graph data, and the existence of edges in the graphs within such databases is often uncertain. Taking this uncertainty into account while mining frequent patterns poses considerable computational challenges. However, doing so is crucial for accurately mining relevant patterns. Existing frequent subgraph mining approaches that consider uncertainty rely on approximation schemes, and are both inefficient and inaccurate. In this paper, we present UFreS, an exact algorithm for mining frequent subgraph patterns from uncertain graph databases. We also introduce Edge-Embedding graphs, the first data structure designed to efficiently and exactly infer the expected support of a subgraph pattern in an uncer-tain graph. Experimental evaluations conducted on real-world datasets show that UFreS is efficient, scalable, and outperforms the existing approaches in terms of runtime, memory usage and accuracy.