In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node ...In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the network.In our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node i.That is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network structure.We show that depending on the aging parameter α two different network topologies can emerge.For α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/m.Moreover,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/m.For α>1,such scaling behavior disappears and the degree distribution is exponential.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.11601294)the Research Project Supported by Shanxi Scholarship Council of China(Grant No.2021-002)+1 种基金the Shanxi Province Science Foundation(Grant No.20210302123466)the 1331 Engineering Project of Shanxi Province。
文摘In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the network.In our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node i.That is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network structure.We show that depending on the aging parameter α two different network topologies can emerge.For α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/m.Moreover,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/m.For α>1,such scaling behavior disappears and the degree distribution is exponential.
