  Community detection is the problem of identifying tightly connected clusters
of nodes within a network. Efficient parallel algorithms for this play a
crucial role in various applications, especially as datasets expand to
significant sizes. The Label Propagation Algorithm (LPA) is commonly employed
for this purpose due to its ease of parallelization, rapid execution, and
scalability. However, it may yield internally disconnected communities. This
technical report introduces GSL-LPA, derived from our parallelization of LPA,
namely GVE-LPA. Our experiments on a system with two 16-core Intel Xeon Gold
6226R processors show that GSL-LPA not only mitigates this issue but also
surpasses FLPA, igraph LPA, and NetworKit LPA by 55x, 10,300x, and 5.8x,
respectively, achieving a processing rate of 844 M edges/s on a 3.8 B edge
graph. Additionally, GSL-LPA scales at a rate of 1.6x for every doubling of
threads.
