Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

Abstract

The problem of network reconstruction under missing and spurious interactions has attracted widespread interests, for it currently appears in a myriad of contexts. However, the existing methods require assuming that prior knowledge is known, which is not always available in realistic situations. The approach of stochastic block models (SBMs) can overcome this limitation, but it is very time-consuming and liable to trap in a local optimum. Specifically, we have to passively wait for several days or longer when it is applied to relatively big networks. In this paper, we study the heuristic mechanism of SBM and reveal that its greedy strategy may lead to the drop of the solution speed and optimality. Accordingly, we propose a novel network reconstruction method, called enhanced SBM (ESBM), with the assistance of the simulated annealing mechanism. By comparing ESBM with SBM in both synthetic and real networks, the experimental results suggest that the reconstructed network's global properties of the ESBM can be closer to those of the true network than those of the SBM. Furthermore, the ESBM can flexibly adjust the convergence time according to the actual speed demands. We are surprised to find that, by slightly sacrificing the prediction accuracy, the calculation speed of ESBM can be more than ten times faster than that of SBM. Our results may also shed light on maximum-likelihood methods for large-scale networks' reconstruction.