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Opinion leaders play a crucial role in closely interconnecting groups and help achieve better group performances in online learning communities. Weighted LeaderRank is superior to other methods in identifying opinion leaders, but there are some limitations in its weighted mechanism. This study further optimizes the weighted mechanism of weighted LeaderRank by introducing users’ initial comprehensive influence and the number of user interactivity. Experimental results show that the improved weighted LeaderRank algorithm can improve the accuracy of opinion leader identification in online learning communities compared with the other two typical algorithms.

Identifying Opinion Leaders with Improved Weighted LeaderRank in Online Learning Communities