  In signed networks, some existing community detection methods treat negative
connections as intercommunity links and positive ones as intracommunity links.
However, it is important to recognize that negative links on real-world
networks also play a key role in maintaining community stability. In this work,
our aim is to identify communities that are not only densely connected but also
harmonious or balanced in terms of the nature of their relationships. Such
communities are more likely to be stable over time and less prone to conflicts.
Consequently, we propose a motif-based method to identify communities by
quantifying the importance of links in the local structural balance. The
results in synthetic and real-world networks show that the proposed method has
a higher performance in identifying the community. In addition, it demonstrates
strong robustness, i.e., remains insensitive to the balance of the network, and
accurately classifies communities in real-world networks.
