In real-world networks,there usually exist a small set of nodes that play an important role in the structure and function of networks.Those vital nodes can influence most of other nodes in the network via a spreading process.While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage,which we call final influencers,recent work proposed the idea of fast influencers,which emphasizes nodes'spreading capacity at the early stage.Despite the recent surge of efforts in identifying these two types of influencers in networks,there remained limited research on untangling the differences between the fast influencers and final influencers.In this paper,we firstly distinguish the two types of influencers:fast-only influencers and final-only influencers.The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage,and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage.Further experiments are based on eight empirical datasets,and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures.We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers.The results demonstrate that with the increase of degree assortativity,the fraction of the fast-only influencers decreases,which indicates that more fast influencers tend to keep their superiority at the final stage.Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications,such as advertisement marketing and epidemic suppressing.

Fang Zhou;Chang Su;Shuqi Xu;Linyuan Lv

Yangtze Delta Region Institute(Huzhou)&Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Huzhou 313001,China;Beijing Computational Science Research Center,Beijing 100193,China

中国物理B（英文版）

[1]Fang Zhou;Chang Su;Shuqi Xu;Linyuan Lv-.Influence fast or later:Two types of influencers in social networks)[J].中国物理B（英文版）,2022(06):50-59

untangling,assortativity

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