The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be...The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61573065and 71731002。
文摘The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.
