Wang Shaokai, Li Xutao, Ye Yunming, Feng Shanshan, Lau Raymond Y K, Huang Xiaohui, Du Xiaolin
Guanghua School of Management, Peking University, Beijing 100871, China.
Harvest Fund Management Co., Ltd., Beijing 100005, China.
Entropy (Basel). 2019 Mar 6;21(3):254. doi: 10.3390/e21030254.
Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.
目前，许多用户参与多个社交网络。在不同网络中识别同一用户，即所谓的锚点链接预测，成为一个重要问题，它可服务于众多应用，例如跨网络推荐、用户画像等。先前的研究主要使用手工构建的结构特征，如果设计不当，可能无法反映内在的结构规律。此外，大多数方法忽略了社交网络的属性信息。在本文中，我们提出了一种新颖的半监督网络嵌入模型来解决该问题。在该模型中，多个网络的每个节点由一个向量表示，用于锚点链接预测，该向量通过将观察到的锚点链接作为半监督信息以及将拓扑结构和属性作为输入来学习。在真实数据集上的实验结果表明，与现有技术相比，所提出的模型具有优越性。
