A faster approximation algorithm for the Steiner problem in graphs
Information Processing Letters
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Context-Aware Object Connection Discovery in Large Graphs
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Structure and attribute index for approximate graph matching in large graphs
Information Systems
REX: explaining relationships between entity pairs
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
We study query processing in large graphs that are fundamental data model underpinning various social networks and Web structures. Given a set of query nodes, we aim to find the groups which the query nodes belong to, as well as the best connection among the groups. Such a query is useful to many applications but the query processing is extremely costly. We define a new notion of Correlation Group (CG), which is a set of nodes that are strongly correlated in a large graph G. We then extract the subgraph from G that gives the best connection for the nodes in a CG. To facilitate query processing, we develop an efficient index built upon the CGs. Our experiments show that the CGs are meaningful as groups and importantly, the meaningfulness of the query results are justifiable. We also demonstrate the high efficiency of CG computation, index construction and query processing.