  We propose a new local community detection algorithm that finds communities
by identifying borderlines between them using boundary nodes. Our method
performs label propagation for community detection, where nodes decide their
labels based on the largest "benefit score" exhibited by their immediate
neighbors as an attractor to their communities. We try different metrics and
find that using the number of common neighbors as benefit scores leads to
better decisions for community structure. The proposed algorithm has a local
approach and focuses only on boundary nodes during iterations of label
propagation, which eliminates unnecessary steps and shortens the overall
execution time. It preserves small communities as well as big ones and can
outperform other algorithms in terms of the quality of the identified
communities, especially when the community structure is subtle. The algorithm
has a distributed nature and can be used on large networks in a parallel
fashion.
