  We develop a decentralized coloring approach to diversify the nodes in a
complex network. The key is the introduction of a local conflict index that
measures the color conflicts arising at each node which can be efficiently
computed using only local information. We demonstrate via both synthetic and
real-world networks that the proposed approach significantly outperforms random
coloring as measured by the size of the largest color-induced connected
component. Interestingly, for scale-free networks further improvement of
diversity can be achieved by tuning a degree-biasing weighting parameter in the
local conflict index.
