LAUSR

In many real applications of semi-supervised learning, the guidance provided by a human oracle might be “noisy” or inaccurate. Human annotators will often be imperfect, in the sense that they can make subjective decisions, they might only have partial knowledge of the task at hand, or they may simply complete a labeling task incorrectly due to the burden of annotation. Similarly, in the context of semi-supervised community finding in complex networks, information encoded as pairwise constraints may be unreliable or conflicting due to the human element in the annotation process. This study aims to address the challenge of handling noisy pairwise constraints in overlapping semi-supervised community detection, by framing the task as an outlier detection problem. We propose a general architecture which includes a process to “clean” or filter noisy constraints. Furthermore, we introduce multiple designs for the cleaning process which use different type of outlier detection models, including autoencoders. A comprehensive evaluation is conducted for each proposed methodology, which demonstrates the potential of the proposed architecture for reducing the impact of noisy supervision in the context of overlapping community detection.