Yunjin Choi

Adaptive community detection via fused l-1 penalty

In recent years, community detection has been an active research area in various fields including machine learning and statistics. While a plethora of works have been published over the past few years, most of the existing methods depend on a predetermined number of communities. Given the situation, determining the proper number of communities is directly related to the performance of these methods. Currently, there does not exist golden rule for choosing the ideal number, and people usually rely on their background knowledge of the domain to make their choices. To address this issue, we propose a community detection method that is equipped with data-adaptive methods of finding the number of underlying communities. Central to our method is a fused l-1 penalty applied on an induced graph from the given data. The proposed method shows promising results.