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 PageRank 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 PageRank 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.

许多用户-对象在线评分系统中的评分可以反映用户喜欢或不喜欢该对象，而在一些在线评分系统中，用户可以直接选择是否喜欢一个对象。所以这些系统可以用有符号二分网络来表示，但是原始的无符号节点评估算法不能直接用于有符号网络。本文提出了签名二部网络的Signed PageRank算法来同时评估对象和用户节点。根据全局信息，节点可以按Signed PageRank值降序排序，结果为SR Ranking。作者分析了真实网络上下节点的特征，发现对于对象，SR Ranking可以提供更合理的排序，结合节点的度和评分，该算法还可以帮助我们识别具有特定评分模式的用户。通过讨论负边缘的位置和对象 SR Ranking 对负边缘的敏感性，作者还探索了负边缘在算法中的重要作用，并解释了为什么差评在真实网络中更重要。