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Who Proposed the Relationship? - Recovering the Hidden Directions of Undirected Social Networks

机译：谁提出了这种关系？ -恢复无向社交网络的隐藏方向

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Together with the sign (positive or negative) and strength (strong or weak), the directionality is also an important property of social ties, though usually ignored in undirected social networks for its invisibility. However, we believe most social ties are natively directed, and the awareness of directionality can improve our understanding about the network structures and further benefit social network analysis and mining tasks. Thus it's appealing to study whether there exist interesting patterns about directionality in social networks and whether we can learn the directions for undirected networks based on these patterns. In this study, we engage in the investigation of directionality patterns on real-world directed social networks and summarize our findings using four consistency hypotheses. Based on these hypotheses, we propose ReDirect, an optimization framework which makes it possible to infer the hidden directions of undirected social ties based on the network topology only. This general framework can incorporate various predictive models under specific scenarios. Furthermore, we show how to improve ReDirect by introducing semi/self-supervision in the framework and how to construct the self-labeled training data using simple but effective heuristics. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we're quite surprising to find that ReDirect can benefit predictive tasks remarkably, with a case study of link prediction. In experiments the redirected networks inferred using ReDirect are proven much more informative than original undirected ones and can improve the prediction performance significantly. It convinces us that ReDirect can be a beneficial general data preprocess tool for various network analysis and mining tasks by uncovering the hidden directions of undirected social networks.
机译：方向性与符号（正或负）和强度（强或弱）一起也是社会联系的重要属性，尽管在定向社会网络中通常由于其不可见而被忽略。但是，我们认为大多数社交关系都是本机定向的，对方向性的了解可以增进我们对网络结构的理解，并进一步使社交网络分析和挖掘任务受益。因此，研究社交网络中是否存在有关方向性的有趣模式以及我们是否可以基于这些模式学习无向网络的方向很有吸引力。在这项研究中，我们参与了现实世界中有针对性的社交网络的方向性模式调查，并使用四个一致性假设总结了我们的发现。基于这些假设，我们提出了ReDirect，这是一个优化框架，它可以仅基于网络拓扑来推断无方向性社会纽带的隐藏方向。该通用框架可以在特定情况下合并各种预测模型。此外，我们展示了如何通过在框架中引入半监督/自我监督来改进ReDirect，以及如何使用简单但有效的启发式方法构造自我标记的训练数据。实验结果表明，即使没有外部信息，我们的方法也可以有效地恢复网络方向。此外，我们很惊讶地发现ReDirect可以通过链接预测的案例研究显着地受益于预测任务。在实验中，使用ReDirect推断出的重定向网络被证明比原始的非定向网络具有更多的信息，并且可以显着提高预测性能。它使我们确信，ReDirect通过发现无方向性社交网络的隐藏方向，可以成为进行各种网络分析和挖掘任务的有益的通用数据预处理工具。

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