Abstract.Community of complex networks has attracted much attention. For the problem, we propose a local heuristic genetic algorithm (LHGA). Mutation of LHGA is combined with local heuristic function from the perspective of networks dynamics and makes convergence rate of this algorithm expressively improved. Extensive tests on artificial networks and real world networks give excellent results.
IntroductionIn recent years, complex networks have attracted much attention. Many complex systems can be represented in term of networks. For example, the Web is a network of HTML pages interconnected by hyperlinks. Internet is described by service and client as nodes, and the connection between them by TCP/IP. A community is thought of as a set of nodes where nodes are densely interconnected and sparsely linked to other parts of the networks. Discovering community structures in complex networks becomes focus in many research fields. Finding community structure of complex network is important to understand function of complex systems they represent.In the paper, we propose a novel algorithm to detect communitywhich is local heuristic geneticalgorithm (LHGA). The remainder of this paper is organized as follows. In the section 2, related work is introduced. LHGA is presented in section 3. In section 4, we give experimentalresult. In section 5, we give our conclusions.