On social-temporal group query with acquaintance constraint
Proceedings of the VLDB Endowment
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
Location-based team recommendation in computer gaming scenarios
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Online team formation in social networks
Proceedings of the 21st international conference on World Wide Web
On socio-spatial group query for location-based social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Searching connected API subgraph via text phrases
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Composing activity groups in social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient bi-objective team formation in social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Efficient algorithms for team formation with a leader in social networks
The Journal of Supercomputing
A strategy of multi-criteria decision-making task ranking in social-networks
The Journal of Supercomputing
Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents
Artificial Intelligence
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Given an expertise social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfy the requirements of the given task but also communicate to one another in an effective manner. To solve this problem, Lappas et al. has proposed the Enhance Steiner algorithm. In this work, we generalize this problem by associating each required skill with a specific number of experts. We propose three approaches to form an effective team for the generalized task. First, we extend the Enhanced-Steiner algorithm to a generalized version for generalized tasks. Second, we devise a density-based measure to improve the effectiveness of the team. Third, we present a novel grouping-based method that condenses the expertise information to a group graph according to required skills. This group graph not only drastically reduces the search space but also avoid redundant communication costs and irrelevant individuals when compiling team members. Experimental results on the DBLP dataset show the teams found by our methods performs well in both effectiveness and efficiency.