Top-k Correlative Graph Mining

Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases is expensive due to the complexity of graph data. In this paper, we study the problem of mining top-k correlative subgraphs in the database, which share similar occurrence distributions with a given query graph. The search space of the problem is prohibitively large since every subgraph in the database is a candidate. We propose an efficient algorithm, TopCor, which mines the top-k correlative graphs by exploring only the candidate graphs in the projected database of a query graph. We develop three key techniques for TopCor: an effective correlation checking mechanism, a powerful pruning criteria, and a set of useful rules for candidate exploration. The three key techniques are very effective in directing the search to those highly correlative candidate graphs. We justify by experiments the effectiveness of the three key techniques and show that TopCor is more than an order of magnitude faster than CGSearch, the state-of-the-art threshold-based correlative graph mining algorithm.