Measuring multiple evolution mechanisms of complex networks

TL;DR

This paper introduces two methods to measure how multiple mechanisms jointly influence the evolution of complex networks, demonstrating that likelihood analysis provides more accurate estimations and revealing diverse influences in real-world networks.

Contribution

The study presents a likelihood-based method for accurately measuring multiple co-evolving mechanisms in complex networks, improving upon existing approaches.

Findings

Likelihood analysis outperforms link prediction in accuracy

Most real-world networks are influenced by both popularity and clustering mechanisms

Different networks exhibit varying weights for the influence of each mechanism

Abstract

Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models of mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights.