Information Dynamics in Evolving Networks Based on the Birth-Death Process: Random Drift and Natural Selection Perspective

Abstract

Dynamic processes in complex networks are crucial for better understanding collective behavior in human societies, biological systems, and the internet. In this paper, we first focus on the continuous Markov-based modeling of evolving networks with the birth-death of individuals. A new individual arrives at the group by the Poisson process, while new links are established in the network through either uniform connection or preferential attachment. Moreover, an existing individual has a limited lifespan before leaving the network. We determine stationary topological properties of these networks, including their size and mean degree. To address the effect of the birth-death evolution, we further study the information dynamics in the proposed network model from the random drift and natural selection perspective, based on assumptions of total-stochastic and fitness-driven evolution, respectively. In simulations, we analyze the fixation probability of individual information and find that means of new connections affect the random drift process but do not affect the natural selection process.

14 pages, 9 figures

References in corpus (22)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Mean-field theory for scale-free random networks
• Networks beyond pairwise interactions: structure and dynamics
• Modern temporal network theory: A colloquium
• Universal behavior in a generalized model of contagion
• Coevolution spreading in complex networks
• Fundamentals of spreading processes in single and multilayer complex networks
• Dynamic information routing in complex networks
• Game-Theoretic Analysis of the Hegselmann-Krause Model for Opinion Dynamics in Finite Dimensions
• Evolutionary dynamics with game transitions
• Spectral entropies as information-theoretic tools for complex network comparison
• Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution
• An Evolutionary Game With the Game Transitions Based on the Markov Process
• Game-theoretic modeling of collective decision-making during epidemics
• Non-consensus opinion models on complex networks
• Protection Degree and Migration in the Stochastic SIRS Model: A Queueing System Perspective
• Temporal network modeling with online and hidden vertices based on the birth and death process
• Random Birth-and-Death Networks
• When greediness and self-confidence meet in a social dilemma
• Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism
• Imitation versus payoff - duality of the decision-making process demonstrates criticality and consensus formation

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