A hybrid artificial immune network for detecting communities in complex networks

One of the challenging problems when studying complex networks is the detection of sub-structures, called communities. Network communities emerge as dense parts, while they may have a few relationships to each other. Indeed, communities are latent among a mass of nodes and edges in a sparse network. This characteristic makes the community detection process more difficult. Among community detection approaches, modularity maximization has attracted much attention in recent years. In this paper, modularity density (D value) has been employed to discover real community structures. Due to the inadequacy of previous mathematical models in finding the correct number of communities, this paper first formulates a mixed integer non-linear program to detect communities without any need of prior knowledge about their number. Moreover, the mathematical models often suffer from NP-Hardness. In order to overcome this limitation, a new hybrid artificial immune network (HAIN) has been proposed in this paper. HAIN aims to use a network's properties in an efficient way. To do so, this algorithm employs major components of the pure artificial immune network, hybridized with a well-known heuristic, to provide a powerful and parallel search mechanism. The combination of cloning and affinity maturation components, a strong local search routine, and the presence of network suppression and diversity are the main components. The experimental results on artificial and real-world complex networks illustrate that the proposed community detection algorithm provides a useful paradigm for robustly discovering community structures.