Fractional Dynamics of Network Growth Constrained by aging Node Interactions
Hadiseh Safdari, Milad Zare Kamali, Amirhossein Shirazi, Moein, Khalighi, Gholamreza Jafari, Marcel Ausloos

TL;DR
This paper introduces a fractional order Barabasi-Albert model incorporating aging effects, revealing how node aging influences network growth and hub formation, with applications demonstrated on Hollywood actor collaboration networks.
Contribution
It develops a fractional differential equation model for network growth with aging, extending the classic BA model and capturing decay in node connectivity over time.
Findings
Aging causes decay in node degrees over time.
Younger nodes have increased chances to become hubs.
Simulation results confirm the fractional model's predictions.
Abstract
In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabasi-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the…
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