Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data

TL;DR

This paper introduces a framework for reconstructing complex networks from binary time series data of social infection dynamics, utilizing maximum likelihood estimation and expectation maximization to improve efficiency and robustness.

Contribution

It presents a novel, efficient method combining statistical inference and vectorization for reconstructing 2-simplex complexes with two- and three-body interactions from binary data.

Findings

High reconstruction accuracy on various network types

Robustness against noise and interference

Effective identification of suitable network types for the framework

Abstract

Based on the binary time series data of social infection dynamics, we propose a general framework to reconstruct 2-simplex complexes with two-body and three-body interactions by combining the maximum likelihood estimation in statistical inference and introducing the expectation maximization. In order to improve the code running efficiency, the whole algorithm adopts vectorization expression. Through the inference of maximum likelihood estimation, the vectorization expression of the edge existence probability can be obtained, and through the probability matrix, the adjacency matrix of the network can be estimated. We apply a two-step scheme to improve the effectiveness of network reconstruction while reducing the amount of computation significantly. The framework has been tested on different types of complex networks. Among them, four kinds of networks can obtain high reconstruction effectiveness. Besides, we study the influence of noise data or random interference and prove the robustness of the framework, then the effects of two kinds of hyper-parameters on the experimental results are tested. Finally, we analyze which type of network is more suitable for this framework, and propose methods to improve the effectiveness of the experimental results.