Proceedings of the 15th international conference on World Wide Web
PROJECT TEAM SELECTION USING FUZZY OPTIMIZATION APPROACH
Cybernetics and Systems
Multi-aspect expertise matching for review assignment
Proceedings of the 17th ACM conference on Information and knowledge management
A team formation model based on knowledge and collaboration
Expert Systems with Applications: An International Journal
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
| Hi-index | 0.00 |
Given a task T , a pool of experts with different skills, and a social network G that captures social relationships and various interactions among these experts, we study the problem of finding a wise group of experts , a subset of , to perform the task. We call this the Expert Group Formation problem in this paper. In order to reduce various potential social influence among team members and avoid following the crowd, we require that the members of not only meet the skill requirements of the task, but also be diverse. To quantify the diversity of a group of experts, we propose one metric based on the social influence incurred by the subgraph in G that only involves . We analyze the problem of Diverse Expert Group Formation and show that it is NP-hard. We explore its connections with existing combinatorial problems and propose novel algorithms for its approximation solution. To the best of our knowledge, this is the first work to study diversity in the social graph and facilitate its effect in the Expert Group Formation problem. We conduct extensive experiments on the DBLP dataset and the experimental results show that our framework works well in practice and gives useful and intuitive results.