Evaluation Of Users’ Influences In Github Based On Improved LeaderRank Algorithm

Github is a common FOSS community and a special social network.It contains complex social relations and various user attributes.By analyzing the influence of users in the community,we can identify key users.And community managers can take positive measures to these users,so that the community can develop continuously and healthily.At present,key users are mainly identified by node importance measurement.Due to the particularity of Git Hub,these algorithms have the problems of low detection efficiency and low accuracy.Most importantly,those algorithms did not consider the interaction between nodes.The existing method cannot sufficiently apply on the weighted network.It is critical to develop an accurate evaluation method about Github users’ influence.Based on the user’s own attributes and the interaction between different users,this paper improves the classic Leader Rank algorithm and proposes a method to identify key users.The results are shown as follows:(1)In view of the fact that the existing algorithms only consider the topological attributes of nodes and can not be accurately applied to Git Hub users’ influence evaluation,this paper proposes a evaluation method based on multi-attribute decision-making and improved Leader Rank algorithm(MADM＿LR).This method based on users’ social behavior,provide multiple attributes targeting to user’s influence and calculate the user’s influence rank with the improved algorithm.The proposed method is applied to Git Hub and compared with the existing methods.Experimental results show that MADM＿LR can not only identify more influential users,but also have better anti-interference performance.(2)In view of the fact that the existing node importance measurement methods in complex networks can’t fully consider the interaction between users,this paper proposes a Git Hub users’ influence evaluation method based on users’ similarity and improved weighted Leader Rank algorithm(US＿WLR).First,we give users’ similarity and H-index for directed networks.The weighted network is constructed to reflect the interactions between nodes based on the accuracy of sorting results.On the basis of the constructed network,this paper proposes an improved weighted Leader Rank algorithm which effectively integrates the initial influence of nodes and the network edges’ weight.The proposed method is applied to the modified users’ follow network,and the results show that US＿WLR can identify users with stronger propagation ability.Combined with user attribute extraction,network construction,model construction and algorithm solving,we give the evaluation method of Git Hub users’ influence.This method can not only combine with the characteristics of users,but also identify the users with higher influence and stronger communication ability efficiently and accurately.The ranking results of users’ influence can be used not only to evaluate the health of community,but also to predict the development trend.Therefore,it has important theoretical significance and application value.The thesis has 14 figures,4 tables,and 85 references.