  We study a novel model for evolution of complex networks. We introduce
information filtering for reduction of the number of available nodes to a
randomly chosen sample, as stochastic component of evolution. New nodes are
attached to the nodes that have maximal degree in the sample, which is a
deterministic component of network evolution process. This fact is a novel for
evolution of scale free networks and depicts a possible new route for modeling
network growth. We present both simulational and theoretical results for
network evolution. The obtained degree distributions exhibit an obvious
power-law behavior in the middle with the exponential cut off in the end. This
highlights the essential characteristics of information filtering in the
network growth mechanisms.
