Type of publication: Only Abstract Type of presentation: Invited Talk for an Organized Session Session: Recent advances in penalized learning methods for complex data Invited by: Seung Jun Shin Title: Adaptive community detection via fused L1 penalty Authors:  Yunjin Choi - National University of Singapore (Singapore) [presenting]
Vincent Tan - National University of Singapore (Singapore)
zhaoqiang Liu - National University of Singapore (Singapore)
Keywords: model selection,network data,non-gaussian models Abstract: In recent years, community detection has been an active research area in various fields including machine learning and statistics. While a plethora of works has 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 a 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 the underlying communities. Central to our method is fused l-1 penalty applied on an induced graph from the given data. The proposed method shows promising results. Comments: