Predicting interval time for reciprocal link creation using survival analysis

The majority of directed social networks, such as Twitter, Flickr and Google+, exhibit reciprocal altruism, a social psychology phenomenon, which drives a vertex to create a reciprocal link with another vertex which has created a directed link toward the former. In existing works, scientists have already predicted the possibility of the creation of reciprocal link—a task known as “reciprocal link prediction”. However, an equally important problem is determining the interval time between the creation of the first link (also called parasocial link) and its corresponding reciprocal link. No existing works have considered solving this problem, which is the focus of this paper. Predicting the reciprocal link interval time is a challenging problem for two reasons: First, there is a lack of effective features, since 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 ever-waiting links (i.e., parasocial links for which a reciprocal link is not formed within the observation period) makes the traditional supervised regression methods unsuitable for such data. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem to a survival analysis task and show through extensive experiments on real-world datasets that survival analysis methods perform better than traditional regression, neural network-based models and support vector regression for solving reciprocal interval time prediction

Uncovering New Links Through Interaction Duration

Link Prediction is the problem of inferring new relationships among nodes in a network that can occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account whether the two nodes we are going to link will benefit from that by having an active interaction over time. For instance, it is better to link two nodes � and � if we know that these two nodes will interact in the social network in the future, rather than suggesting �, who may never interact with �. Thus, the longer the interaction is estimated to last, i.e., persistent interactions, the higher the priority is for connecting the two nodes. This current thesis focuses on the problem of predicting how long two nodes will interact in a network by identifying potential pairs of nodes (�, �)that are not connected, yet show some Indirect Interaction. “Indirect Interaction” means that there is a particular action involving both the nodes depending on the type of network. For example, in social networks such as Facebook, there are users that are not friends but interact with other user’s wall posts. On the Wikipedia hyperlink network, it happens when readers navigate from page � to page � through the search box (on the top right corner of page �), and there is no explicit link on page � to �. This research explores cases that involved multiple interactions between � and � during an observational time interval [��, ��). Two supervised learning approaches are proposed for the problem. Given a set of network-based predictors, the basic approach consists of learning a binary classifier to predict whether or not an observed Indirect Interaction will last in the future. The second and more fine-grained approach consists of estimating how long the interaction will last by modeling the problem via Survival Analysis or as a Regression task. Once the duration is estimated, this information is leveraged for the Link Prediction task. Experiments were performed on the longitudinal Facebook network and wall interactions dataset, and Wikipedia Clickstream dataset to test this approach of predicting the Duration of Interaction and Link Prediction. Based on the experiments conducted, this study’s results show that the fine-grained approach performs the best with an AUROC of 85.4% on Facebook and 77% on Wikipedia for Link Prediction. Moreover, this approach beats a Link Prediction model that does not consider the Duration of Interaction and is based only on network properties, and that performs with an AUROC of 0.80 and 0.68 on Facebook and Wikipedia, respectively