Local Heuristic Genetic Algorithm for Discovering Community of Complex Networks

Abstract

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.

Community-Detection Method of Complex Network Based on Node Influence Analysis

“…Since Girven and Newman introduced the concept of community in 2002 [8], many community-detection methods have been proposed [10]. Among them, the communitydetection methods for undirected complex networks have been widely studied, which can be broadly classified into graph-partitioning methods [11,12], clustering methods [13,14] and methods based on heuristic algorithms [15,16]. Some of these methods require early identification of community centers, while others do not.…”