Keyword search over a graph finds a substructure of the graph containing all or some of the input keywords. Most of previous methods in this area find connected minimal trees that cover all the query keywords. Recently, it has been shown that finding subgraphs rather than trees can be more useful and informative for the users. However, the current tree or graph based methods may produce answers in which some content nodes (i.e., nodes that contain input keywords) are not very close to each other. In addition, when searching for answers, these methods may explore the whole graph rather than only the content nodes. This may lead to poor performance in execution time. To address the above problems, we propose the problem of finding r-cliques in graphs. An r-clique is a group of content nodes that cover all the input keywords and the distance between each two nodes is less than or equal to r. An exact algorithm is proposed that finds all r-cliques in the input graph. In addition, an approximation algorithm that produces r-cliques with 2-approximation in polynomial delay is proposed. Extensive performance studies using two large real data sets confirm the efficiency and accuracy of finding r-cliques in graphs.
['Mehdi Kargar', 'Aijun An']
Keyword search in graphs: finding r-cliques