Inferring Directions of Undirected Social Ties

Inferring Directions of Undirected Social Ties
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推断无向社会关系的方向

DOI:
10.1109/tkde.2016.2605081
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发表时间:
2016-12
影响因子:
8.9
通讯作者:
Wang Changping
Wang Changping
中科院分区:
计算机科学2区
文献类型:
--
作者:
Zhang Jun;Wang Chaokun;Wang Jianmin;Yu Jeffrey Xu;Chen Jun;Wang Changping

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该方向性是社会关系的重要但固有的特性,尽管由于其隐身性,通常在无方向性的社交网络中被忽略。但是,我们认为大多数社会关系都是本地的,并且对方向的看法可以
The directionality is a significant but inherent property of social ties, though usually ignored in undirected social networks due to its invisibility. However, we believe most social ties are natively directed, and the perception of directionality can improve our understanding about the network structures and further benefit other tasks upon social networks. In this study, we address the latent tie direction inference problem in undirected social networks. We engage in the investigation of directionality on real-world large-scale directed social networks and summarize our findings using four patterns. Upon that we propose a family of ReDirect approaches, including ReDirect-N, ReDirect-T and ReDirect-One, to inferring the hidden directions of undirected social ties based on the network topology only. ReDirect can incorporate with other predictive tasks, and introduce supervision to improve performance. We also present a simple but effective strategy to construct self-labeled data. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we find the ReDirect approaches can benefit the predictive tasks remarkably in an experimental study on link prediction. The ReDirect family can be a beneficial general data preprocess tool for various network analysis tasks by uncovering the hidden directions.
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