A Hebbian approach to complex-network generation

Through a redefinition of patterns in a Hopfield-like model, we introduce and develop an approach to model discrete systems made up of many, interacting components with inner degrees of freedom. Our approach highlights the intrinsic connection between the kind of interactions among components and the emergent topology describing the system itself; also, it allows to effectively address the statistical mechanics on the resulting networks. Indeed, a wide class of analytically treatable, weighted random graphs with a tunable level of correlation can be recovered and controlled. We especially focus on the case of imitative couplings among components endowed with similar patterns (i.e. attributes), which naturally gives rise to small-world effects. We also solve the thermodynamics (at a replica symmetric level) by extending the double stochastic stability technique: free energy, self-consistency relations and fluctuation analysis for a picture of criticality are obtained. Finally, applications are considered, with particular attention to the agreement among the non-trivial features predicted by the theory and the experimental findings. Copyright (C) EPLA, 201