Evidential Reconstruction of Network from Time Series

TL;DR

This paper introduces an evidential reasoning framework based on Dempster-Shafer theory to accurately reconstruct complex network topologies from time series data, demonstrating robustness and applicability across various models and real-world datasets.

Contribution

It presents a novel evidential reconstruction method that effectively integrates multi-source information for network inference, outperforming existing approaches in accuracy and robustness.

Findings

High reconstruction accuracy across different network models

Robust performance with increasing network size and density

Effective application to real-world datasets

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.