Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Keyword proximity search in complex data graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Querying Communities in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Keyword Search in Databases
Discovering top-k teams of experts with/without a leader in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the VLDB Endowment
Searching connected API subgraph via text phrases
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Density index and proximity search in large graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
NeMa: fast graph search with label similarity
Proceedings of the VLDB Endowment
ROU: advanced keyword search on graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Strong simulation: Capturing topology in graph pattern matching
ACM Transactions on Database Systems (TODS)
Top-K nearest keyword search on large graphs
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
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.