Efficient Subgraph Search over Large Uncertain Graphs

Retrieving graphs containing a query graph from a large graph database is a key task in many graph-based applications, including chemical compounds discovery, protein complex prediction, and structural pattern recognition. However, graph data handled by these applications is often noisy, incomplete, and inaccurate because of the way the data is produced. In this paper, we study sub-graph queries over uncertain graphs. Specifically, we consider the problem of answering threshold-based probabilistic queries over a large uncertain graph database with the possible world semantics. We prove that problem is #P-complete, therefore, we adopt a filtering-and-verification strategy to speed up the search. In the filtering phase, we use a probabilistic inverted index, PIndex, based on subgraph features obtained by an optimal feature selection process. During the verification phase, we develop exact and bound algorithms to validate the remaining candidates. Extensive experimental results demonstrate the effectiveness of the proposed algorithms.