Fast complex network clustering algorithm using local detection

Recently, complex networks are always very huge and take on distributed nature. Therefore it is gradually becoming instant requirement to propose fast network clustering algorithms in the sight of local view. For the problem, this paper deduces a local objective function f aiming to each node in the network, which is based on the profound analysis on network modularity function Q, and proves that Q is monotone increasing with function f of any node, and then proposes a fast network clustering algorithm (FNCA) by using local optimization. In this algorithm, each node optimizes its own objective function f by only local information, and all the nodes collectively optimize function Q to detect network community structure. Both efficiency and effectiveness of algorithm FNCA are tested against computer-generated and real-world networks. Experimental result shows that this algorithm is better than some excellent network clustering algorithms in term of these two respects.