1 research outputs found
Robust network community detection using balanced propagation
Label propagation has proven to be an extremely fast method for detecting
communities in large complex networks. Furthermore, due to its simplicity, it
is also currently one of the most commonly adopted algorithms in the
literature. Despite various subsequent advances, an important issue of the
algorithm has not yet been properly addressed. Random (node) update orders
within the algorithm severely hamper its robustness, and consequently also the
stability of the identified community structure. We note that an update order
can be seen as increasing propagation preferences from certain nodes, and
propose a balanced propagation that counteracts for the introduced randomness
by utilizing node balancers. We have evaluated the proposed approach on
synthetic networks with planted partition, and on several real-world networks
with community structure. The results confirm that balanced propagation is
significantly more robust than label propagation, when the performance of
community detection is even improved. Thus, balanced propagation retains high
scalability and algorithmic simplicity of label propagation, but improves on
its stability and performance