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

作　　者：Farnoush Farhadi Maryam Sorkhi Sattar Hashemi Ali Hamzeh 

机构地区：[1]School of Electrical and Computer Engineering,Shiraz University,Shiraz,Iran

出　　处：《Journal of Computer Science & Technology》2012年第3期577-590,共14页计算机科学技术学报（英文版）

摘　　要：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 axe 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.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 axe 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.

关 键 词：expert team social network team formation 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]