Generating realistic scaled complex networks

TL;DR

This paper introduces ReCoN, a new scalable network generator that produces realistic, large-scale networks matching original structures, outperforming existing models in preserving network properties.

Contribution

The paper presents ReCoN, a novel network generator capable of creating realistic, large-scale networks and fitting existing models to original data, advancing network modeling techniques.

Findings

ReCoN often outperforms state-of-the-art models.

ReCoN effectively preserves micro- and macro-scale properties.

ReCoN can generate networks much larger than the original.

Abstract

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.