  Recently, the sizes of networks are always very huge, and they take on
distributed nature. Aiming at this kind of network clustering problem, in the
sight of local view, this paper proposes a fast network clustering algorithm in
which each node is regarded as an agent, and each agent tries to maximize its
local function in order to optimize network modularity defined by function Q,
rather than optimize function Q from the global view as traditional methods.
Both the efficiency and effectiveness of this algorithm are tested against
computer-generated and real-world networks. Experimental result shows that this
algorithm not only has the ability of clustering large-scale networks, but also
can attain very good clustering quality compared with the existing algorithms.
Furthermore, the parameters of this algorithm are analyzed.
