LAUSR

Abstract In this paper, we propose a general scheme of Functional Network, by considering granularity of information and time delay. Functional Networks (FNs) are a relatively recent alternative to standard Neural Networks (NNs). They have shown better performance in comparison to performance of NNs. Data granulation used in the development of NNs allows for the formation of more efficient and transparent architectures. Time delay models have been recognized to be more realistic constructs of real-world systems. By keeping these observations in mind, we revise the usual design scheme of FN by casting it in the settings of information granules, defining a different learning algorithm, and by introducing time delay. Under some assumptions, we discuss some dynamical properties of the proposed model, in particular those concerning asymptotic stability and Neimark–Sacker bifurcation. Finally, we present an application of the proposed method to the problem of sign prediction in social networks. The results reported against those obtained by the state-of-the-art method show good performance of the proposed approach.