Prediction and inference in complex networks: a brief review and perspectives

TL;DR

This paper reviews recent advances in inference and prediction methods for complex networks, highlighting key developments in sampling, comparison, link prediction, and reconstruction from time series, and discusses future research directions.

Contribution

It provides a comprehensive overview of recent methodological advances and emerging approaches integrating statistical and machine learning techniques for complex network analysis.

Findings

Summarizes key methodological developments in network inference.

Highlights emerging approaches combining statistical and machine learning.

Outlines promising future research directions in the field.

Abstract

Inference and prediction are fundamental to the study of complex systems, where network data are often incomplete, inaccurate or obtained indirectly. In this paper, we review recent advances in network sampling and comparison, as well as in link prediction and network reconstruction from time series. We summarise key methodological developments and emerging approaches that integrate statistical and machine learning perspectives. We also outline promising research directions for enhancing the inference and prediction of complex networked systems.