Evolving Model of Weighted Networks Inspired by Scientific Collaboration Networks

TL;DR

This paper introduces an evolutionary model for weighted networks inspired by scientific collaboration, incorporating old vertices reconnecting and path-based attachment, which enhances clustering and replicates empirical network properties.

Contribution

It presents a novel weighted network model that includes reconnection dynamics and path-based preferential attachment, extending traditional models like BA.

Findings

The model exhibits scale-free degree and weight distributions.

It significantly increases the clustering coefficient.

Results align qualitatively with empirical collaboration networks.

Abstract

Inspired by scientific collaboration networks, especially our empirical analysis of the network of econophysicists, an evolutionary model for weighted networks is proposed. Both degree-driven and weight-driven models are considered. Compared with the BA model and other evolving models with preferential attachment, there are two significant generalizations. First, besides the new vertex added in at every time step, old vertices can also attempt to build up new links, or to reconnect the existing links. The reconnection between both new-old and old-old nodes are recorded and the connecting times on every link is converted into the weight of the link. This provides a natural way for the evolution of edge weight. Second, besides degree and the weight of vertices, a path-related local information is also used as a reference in the preferential attachment. The path-related preferential attachment mechanism significantly increases the clustering coefficient of the network. The model shows the scale-free phenomena in degree and weight distribution. It also gives well qualitatively consistent behavior with the empirical results.