Proceedings:
Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media
Volume
Issue:
Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media
Track:
Poster Papers
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Abstract:
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).
DOI:
10.1609/icwsm.v11i1.14961
ICWSM
Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media