The goal of survival clustering is to map subjects (e.g., users in a social network, patients in a medical study) to K clusters ranging from low-risk to high-risk. Existing survival methods assume the presence of clear end-of-life signals or introduce them artificially using a pre-defined timeout. In this paper, we forego this assumption and introduce a loss function that differentiates between the empirical lifetime distributions of the clusters using a modified Kuiper statistic. We learn a deep neural network by optimizing this loss, that performs a soft clustering of users into survival groups. We apply our method to a social network dataset with over 1M subjects, and show significant improvement in C-index compared to alternatives.
Pros:
The paper is a nice read, clearly written, and its originality is well stated by the authors, “addressing the lifetime clustering problem without end-of-life signals for the first time”. I do not feel experienced enough in the field to evaluate the significance of this work.
The approach proposed in the manuscript is mainly based on a newly-designed nonparametric loss function using the Kuiper statistic and uses a feed-forward neural network to optimize the loss function. This approach does challenge some traditional assumptions, such as the presence of end-of-life signals or the artificial defined timeouts. Instead of giving a clear end-of-life signal, the authors specify a probability of end-of-life that permits us to take into account the associated uncertainty. By analyzing a large-scale social network dataset, it is shown that the proposed method performs better on average than the other two traditional models.
Cons:       
I think that the main drawback of the paper is that the structure of the neural network and the deep learning techniques used for optimizing the loss function are not explained in sufficient detail.