To identify potential stars in social networks, the idea of combining member promotion with skyline operator attracts people’s attention. Some algorithms have been proposed to deal with this problem so far, such as skyline boundary algorithms in unequal-weighted social networks. We propose an improved member promotion algorithm by presenting ReputationRank based on eigenvectors as well as Influence and Activeness and introduce the concept of skyline distance. Furthermore, we perform skyline operator over non-skyline set and choose the infra-skyline as our candidate set. The added ReputationRank helps a lot to describe the importance of a member while the skyline distance assists us to obtain the necessary condition for not being dominated so that some meaningless plans can be pruned. Experiments on the DBLP and WikiVote datasets verify the effectiveness and efficiency of our proposed algorithm. Treating the infra-skyline set as candidate set reduces the number of candidates. The pruning strategies based on dominance and promotion cost decrease the searching space.

中文翻译：

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在社交网络中使用天际线运营商将 ReputationRank 添加到会员推广中。

为了发掘社交网络中的潜在明星，将会员推广与天际线运营商相结合的想法引起了人们的关注。到目前为止，已经提出了一些算法来处理这个问题，例如不等权社交网络中的天际线边界算法。我们提出了一种改进的会员提升算法，提出了基于特征向量以及影响力和活跃度的 ReputationRank，并引入了天际线距离的概念。此外，我们在非天际线集上执行天际线算子，并选择下天际线作为我们的候选集。增加的 ReputationRank 对描述成员的重要性有很大帮助，而天际线距离则帮助我们获得不被支配的必要条件，从而可以修剪一些无意义的计划。DBLP 和 WikiVote 数据集上的实验验证了我们提出的算法的有效性和效率。将 infra-skyline 集视为候选集会减少候选集的数量。基于优势和提升成本的剪枝策略减少了搜索空间。