An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before. Given a task and a graph of experts where each expert possesses some skills, we tend to find an effective team of experts who are able to accomplish the task. This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is, considering each expert has the minimum required level of skill. Here, we generalize the problem in multiple perspectives. First, a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors. Second, the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Third, the existing RarestFirst algorithm is extended to more generalized version, and finally the cost definition is customized to improve the efficiency of selected team. Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness, our proposed framework is achieved well in practice.