A Memetic Algorithm for Community Detection in Signed Networks

Community discovery (i.e. community detection) in signed networks is a division of nodes, such that the edges in the communities are positive and the edges between the communities are negative. Davis and Harary have solved the problem of community detection when a signed graph is balanced or weakly balanced. When the signed network is unbalanced, community detection becomes very complex. In this paper, we propose a novel memetic algorithm (MA) called MACD-SN for community partition (i.e. community detection) in signed networks. Firstly, we present a novel initialization algorithm used in initialization of MACD-SN. This method can accelerate the convergence rate of MACD-SN algorithm. Next, in addition to using frequently-used variation operation (in this paper, variation and mutation are interchangeable), this paper presents a novel crossover operation and a novel variation operation, which contributes to increasing the correctness of the MACD-SN algorithm's operation result and reduces its running time. Lastly, this paper proposes a new local search algorithm, which may enable the algorithm's result to jump away the local best result with a certain probability and draw near the global best result quickly. For testing the performance of MACD-SN algorithm, we have done many experiments using five kinds of synthetic signed networks and five real-world signed networks. The test outcomes show that the proposed algorithm is valid and efficient for signed network cluster partition (i.e. community detection).