How Fast Will You Get a Response? Predicting Interval Time for Reciprocal Link Creation
How Fast Will You Get a Response? Predicting Interval Time for Reciprocal Link Creation
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DOI：
10.1609/icwsm.v11i1.14961
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发表时间：
2017-05
影响因子：
2.4
通讯作者：
Vachik S. Dave;M. Hasan;Chandan K. Reddy
中科院分区：
文献类型：
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作者：
Vachik S. Dave;M. Hasan;Chandan K. Reddy
近年来，互惠链接预测得到了数据挖掘和社会网络分析研究人员的关注，他们解决了这个问题作为一个二元分类任务。然而，预测建立相互链接的间隔时间也很重要。这是一个具有挑战性的问题，原因有两个：第一，缺乏有效的特征，因为众所周知的链接预测特征是为无向网络和二进制分类任务设计的，因此它们不能很好地用于间隔时间预测;第二，删失数据实例的存在使得传统的监督回归方法不适合解决这个问题。本文提出了一种互惠链路间隔时间预测任务的解决方案。我们将这个问题映射到生存分析框架中，并通过对真实世界数据集的大量实验表明，生存分析方法的性能优于传统回归，基于神经网络的模型和支持向量回归（SVR）。
In the recent years, reciprocal link prediction has received some attention from the data mining and social network analysis researchers, who solved this problem as a binary classification task. However, it is also important to predict the interval time for the creation of reciprocal link. This is a challenging problem for two reasons: First, the lack of effective features, because well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of censored data instances makes the traditional supervised regression methods unsuitable for solving this problem. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem into survival analysis framework and show through extensive experiments on real-world datasets that, survival analysis methods perform better than traditional regression, neural network based model and support vector regression (SVR).