An efficient algorithm for top-k proximity query on uncertain graphs

The uncertainty of graph data is widely existed in practice, researching on efficient query processing algorithms on uncertain graphs has significant meanings. This paper propose a new kind of query on uncertain graphs-proximity query. Given a query label set R and a distance constrain σ, executing proximity query on an uncertain graph G is to find some vertex subsets of G that whose label set contains R and for any two vertices in it, the distance between them can not exceed σ. To solve this issue, first, we propose a distance measure function named "Reliable Expectation Distance", and then design an efficient proximity relations graphs index and the proximity query on uncertain graphs is equally converted to the clique finding on proximity relations graphs. Finally, we propose a tree-based searching algorithm to finish the query processing. Theoretical and experimental results show that the proposed algorithm can efficiently retrieve the top-k proximity query results.