# Ranking in evolving complex networks

## TL;DR

This paper reviews ranking algorithms for complex networks, emphasizing the importance of temporal dynamics in evolving networks and their impact on improving ranking accuracy and predictive capabilities.

## Contribution

It provides a comprehensive survey of static and time-aware ranking algorithms, highlighting recent advances and their applications to evolving complex networks.

## Key findings

- Static algorithms like PageRank have limitations on evolving networks.
- Time-aware algorithms improve prediction of network traffic and future links.
- Including temporal data enhances identification of significant nodes.

## Abstract

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.