A Two-Stage Approach for Social Identity Linkage based on an Enhanced Weighted Graph Model
A Two-Stage Approach for Social Identity Linkage based on an Enhanced Weighted Graph Model
复制标题
基于增强加权图模型的社会身份联动两阶段方法
DOI：
10.1007/s11036-019-01456-8
复制
发表时间：
2020
影响因子：
3.8
通讯作者：
Xiaohong Guan
中科院分区：
文献类型：
--
作者：
Tao Qin;Zhaoli Liu;Shangcang Li;Xiaohong Guan
社会身份关联是指在不同的社交网络中识别属于同一个人的账户。这项工作可以帮助构建更完整的社交配置文件，这对许多社交应用程序都很有价值。在本文中，我们提出了一个两阶段的方法来提高大规模的社会身份联系的效率和准确性。第一阶段处理种子集富集问题，并专注于以更高的精度探索更大的种子集。第二阶段处理全局传播问题，并专注于以较低的计算量找到更多的匹配对。此外，我们提出了一个增强的加权图模型，深入研究的结构特征。我们还开发了一种属性表示方法，以减少缺失属性的影响。最后，我们从两个流行的社交网络在中国收集的数据集的基础上评估我们的方法。实验结果表明，该方法优于其他最先进的算法。
Social identity linkage refers to identify the accounts belong to the same person across different social networks. This work can assist in building more complete social profiles, which is valuable for many social-powered applications. In this paper, we propose a two-stage approach to improve the efficiency and accuracy of large-scale social identity linkage. The first stage deals with the seed set enrichment problem and focuses on exploring a larger set of seeds with greater precision. The second stage deals with the global propagation problem and focuses on finding more matched pairs with lower computation. Moreover, we propose an enhanced weighted graph model to deeply investigate the structural characteristics. We also develop an attribute representation method to reduce the impact of missing attributes. Finally, we evaluate our method based on the datasets collected from two popular social networks in China. And the experimental results demonstrate that our method outperforms other state of the art algorithms.