Evidential Reconstruction of Network from Time Series

Abstract

Reconstructing the topology of complex networks from observational data remains a central challenge in network science. Here we propose a framework that is based on the Dempster-Shafer evidence theory to infer network structures directly from time series. By integrating multi-source information within an evidential reasoning scheme, the method captures underlying interaction patterns with high fidelity. Tests on three representative network models Barabasi-Albert Network, Erdos-Renyi Network, and Watts-Strogatz Network-show that the reconstruction accuracy is consistently high and remains robust against increases in network size and density. The application of the framework to real-world datasets from diverse domains further confirms its stability and general applicability. These results suggest that evidential reasoning offers a powerful and scalable approach for uncovering the structural organization of complex systems, especially when dealing with uncertain or incomplete multi-source data.