Adopting Different Strategies for Improving Local Community Detection: A Comparative Study

In recent decades, several community detection methods have been proposed to uncover densely connected groups in a network. In many cases, there is the necessity of detecting such a group around a seed node that may be of particular topological importance or the external knowledge enable that node to be significant for the specific subgroup. In this way, local community detection algorithms can discover subgroups faster than global community detection algorithms that cover the entire network. This study aims to introduce new variants of an existing local community detection algorithm that uncover a single community and establish the positive aspects of identifying better communities. We experimentally evaluate our proposed methods in synthetic and real datasets compared to the baseline method, three state-of-the-art methods in local community detection, and a global community detection method.