结合全局种子最优局部扩展的跨网络用户识别

Cross-Network User Identification Using Global Seed and Optimal Local Extension

跨网络用户匹配的目的是识别不同社交网络上属于同一用户的不同账户,在好友推荐、网络安全和链路预测等方面有重要意义。现有方法通常利用部分已知匹配用户,迭代识别其余待匹配用户。然而,目前大部分方法受限于已知匹配用户的数量,无法在较低的时间内精准地识别用户。提出了结合全局种子最优局部扩展的跨网络用户识别方法(GLE)。首先,为有效解决冷启动问题,提出了全局种子扩展模型(GSE)来丰富已知匹配用户数量;然后,为了在较低的时间代价上确保较高的准确性,提出了最优局部扩展模型来找到最优候选匹配对。最后,实验结果表明,该算法可显著提高用户识别的召回率和准确率,具有较低的时间开销,并解决了已知匹配用户数量不足时的识别问题。

Cross-network user identification aims to identify the accounts owned by the same user across multiple networks, which is significant in friend recommendation, network security and link prediction. Existing methods mainly make full use of a small set of seed users and iteratively identify the other users. However, limited by the scale of seed users, these methods cant reach a satisfactory accuracy with low time complexity. A method of crossnetwork user identification using global seed and optimal local extension(GLE) is proposed. Firstly, in order to effectively solve the cold start problem, this paper proposes a global seed expansion method(GSE) to expand the seed set. Secondly, to ensure higher accuracy at a lower time cost, this paper proposes a local search range expansion method for candidate searching. Finally, experiments demonstrate that this method can significantly improve the recall and precision of user identification at a lower time cost, and effectively solves the identification problem when the scale of seed users is insufficient.