Efficient processing of group-oriented connection queries in a large graph
Proceedings of the 18th ACM conference on Information and knowledge management
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The community-search problem and how to plan a successful cocktail party
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An agglomerative query model for discovery in linked data: semantics and approach
Procceedings of the 13th International Workshop on the Web and Databases
Mining and explaining relationships in wikipedia
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Structure and attribute index for approximate graph matching in large graphs
Information Systems
Effective and efficient keyword query interpretation using a hybrid graph
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Querying large graph databases
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
| Hi-index | 0.00 |
Given a large graph and a set of objects, the task of object connection discovery is to find a subgraph that retains the best connection between the objects. Object connection discovery is useful to many important applications such as discovering the connection between different terrorist groups for counter-terrorism operations. Existing work considers only the connection between individual objects; however, in many real problems the objects usually have a context (e.g., a terrorist belongs to a terrorist group). We identify the context for the nodes in a large graph. We partition the graph into a set of communities based on the concept of modularity, where each community becomes naturally the context of the nodes within the community. By considering the context we also significantly improve the efficiency of object connection discovery, since we break down the big graph into much smaller communities. We first compute the best intra-community connection by maximizing the amount of information flow in the answer graph. Then, we extend the connection to the inter-community level by utilizing the community hierarchy relation, while the quality of the inter-community connection is also ensured by modularity. Our experiments show that our algorithm is three orders of magnitude faster than the state-of-the-art algorithm, while the quality of the query answer is comparable.