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Title: How Fast Will You Get a Response? Predicting Interval Time for Reciprocal Link Creation
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).
Award ID(s):
1707498 1646881 1619028
Publication Date:
Journal Name:
Proceedings of the Eleventh International Conference on Web and Social Media (ICWSM)
Sponsoring Org:
National Science Foundation
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