A Survey of Evolving Models for Weighted Complex Networks based on their Dynamics and Evolution

TL;DR

This survey reviews evolving models of weighted complex networks, focusing on their dynamics, structures, and how synthetic models compare with real-world networks across various types.

Contribution

It provides a comprehensive overview of models for weighted network evolution, including different network types and their properties, highlighting recent advances and research directions.

Findings

Synthetic networks exhibit properties similar to real-world networks.

Different models capture diverse structural features.

Weighted networks evolve under various underlying mechanics.

Abstract

For decades, complex networks, such as social networks, biological networks, chemical networks, technological networks, have been used to study the evolution and dynamics of different kinds of complex systems. These complex systems can be better described using weighted links as binary connections do not portray the complete information of the system. All these weighted networks evolve in a different environment by following different underlying mechanics. Researchers have worked on unraveling the evolving phenomenon of weighted networks to understand their structure and dynamics. In this chapter, we discuss the evolution of weighted networks and evolving models to generate different types of synthetic weighted networks, including undirected, directed, signed, multilayered, community, and core-periphery structured weighted networks. We further discuss various properties held by generated synthetic networks and their similarity with real-world weighted networks.