LAUSR

Detecting communities of highly internal and low external interactions in dynamically evolving networks has become increasingly important owing to its wide applications in divers fields. Conventional solutions based on static community detection approaches treat each snapshot of dynamic networks independently, which may fragment communities in time (Aynaud T and Guillaume J L 2010 8th Int. Symp. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (IEEE) pp 513–9), resulting in the problem of instability. In this work, we develop a novel dynamic community detection algorithm by leveraging the encoding–decoding scheme present in a succinct network representation method to reconstruct each snapshot via a common low-dimensional subspace, which can remove non-significant links and highlight the community structures, resulting in the mitigation of community instability to a large degree. We conduct experiments on simulated data and real social networking data with ground truths (GT) and compare the proposed method with several baselines. Our method is shown to be more stable without missing communities and more effective than the baselines with competitive performance. The distribution of community size in our method is more in line with the real distribution than those of the baselines at the same time.