Detecting Local Communities in Networks with Edge Uncertainty

Abstract

In this work, we focus on the problem of local community detection with edge uncertainty. We use an estimator to cope with the intrinsic uncertainty of the problem. Then we illustrate with an example that periphery nodes tend to be grouped into their neighbor communities in uncertain networks, and we propose a new measure $\mathcal{K}$ to address this problem. Due to the very limited publicly available uncertain network datasets, we also put forward a way to generate uncertain networks. Finally, we evaluate our algorithm using existing ground truth as well as based on common metrics to show the effectiveness of our proposed approach.