Research On Identifying Influential Spreaders In Reversible Processes On Complex Networks

Identifying influential spreaders is an important topic in the spreading dynamics on complex networks.For different dynamical processes and complex network functions,the criteria to identify important nodes are diverse.Previous researches focused on the irreversible propagation processes,which can be categorized into the Susceptible-Infected-Recovered(SIR)model type.These models are irreversible spreading processes and the system will reach a final state without infected nodes.However,some spreading processes are reversible with repeated infections,such as gonorrhea spreading propagation on sexual intercourse networks and the rise and fall of stock prices on financial stock market networks.These reversible processes can be described by the SIS spreading dynamics,where a node transfers between infection state and recovery state.In this dissertation,we focus on the reversible SusceptibleInfected-Susceptible(SIS)spreading processes,and study the methods of ranking the influence of nodes and identifying influential spreaders in real-world networks.Firstly,we study the problem of identifying vital nodes in the SIS spreading process in complex networks.We articulate a single-node control model to evaluate the influence of nodes in the reversible spreading system.By considering network structural and reversible spreading characteristics,we propose a new measure to quantify the node influence based on its neighbors' centrality and infection risk.By applying the commonly used centralities such as degree and coreness,this new measure can identify the most influential spreaders more accurately than the benchmark centralities.The proposed single-node control model and ranking method open up a new idea in identifying influential spreaders and validate the necessity of introducing the dynamical state in the reversible systems.Secondly,except for structural centrality,the nodes' dynamical states play a significant role in their spreading influence in the reversible spreading processes.By integrating the number of outgoing edges and infection risks of node's neighbors into structural centrality,a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence.The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders,respectively.We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders.Notably,compared with the positive effect of the local structural characteristic,slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance.Lastly,we extend the single-node control model to the cascade model.In the reversible failure recovery processes for financial stock networks,the single-node control model can still effectively quantify the node influence.By analyzing the structural characteristics of the stock network,we improve the k-shell algorithm.By the s-shell decomposition method,we can distinguish the shell structure more precisely.Combined with the NDIC index,we find that the improved new index can identify influential nodes in the financial stock networks accurately.The results demonstrate the necessity of considering neighbors' structures and dynamic states in the reversible processes of node influence identification.Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.This dissertation provides new ideas and methods for the identification of the influential nodes in the reversible spreading process.