Discerning Influential Spreaders in Complex Networks by Accounting the Spreading Heterogeneity of the Nodes

Abstract

Centrality is driven immunization is one of the best ways to prevent massive outbreaks (e.g., rumors and computer viruses) on complex networks, for it can effectively block the important diffusion paths to delay the propagation process. However, most of the previous strategies only consider the topology factor while the individual heterogeneity is widely found in the real world (e.g., different entities have different behaviors and tendencies in transmitting new information) and has an important influence on the propagation process. In this paper, we propose a new heterogeneity oriented centrality that is measured by two heterogeneity factors and one topology factor. First, a heterogeneity factor to describe the frequency of nodes activities (activity rank) is introduced; then a novel conception spread rank is first defined and explained to characterize the spread ability of nodes; finally, one topology factor is combined with the heterogeneity factors. After conducting comprehensive experiments on synthetic and real-world networks by using an interactive email model, the results show that HO centrality could delay the propagation most remarkably than the existing strategies. Therefore, the heterogeneity attributes of nodes should be taken into account when we design a network immunization strategy.

Modelling and analysing the coexistence of dual dilemmas in the proactive vaccination game and retroactive treatment game in epidemic viral dynamics

“…In a coevolutionary adaptation process, it is important to know whether the social dual-dilemma exists under certain combinations of the model parameters, such as the vaccination effectiveness, treatment efficiency and their associated costs. Unlike simple 2 by 2 games in which the so-called dilemma strength (DS) can be explicitly defined [1], a real social dilemma typically observed in the vaccination game [2,[5][6][7][36][37][38][39][40][41][42][43][44], traffic flow [46][47][48][49][50][51][52] and others may have a time-variable game structure. In the vaccination and traffic games, this structure is mainly influenced by the disease-spreading and traffic flow dynamics, respectively.…”

“…The human decision-making process is affected by the cost and risk of the vaccine, selfopinion, networks and neighbours' decisions; therefore, how vaccine acquiescence is influenced by various factors must be investigated [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. According to prior studies, a game approach to epidemiological vaccination can fairly predict the infection risk in both vaccinated and nonvaccinated individuals [49][50][51][52].…”

Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks

“…By utilising the structure of the local network around a node and the influence feedback from the node's closest neighbors, Zhao et al [28] concentrated on normalised local centrality. Xin et al [29] proposed a heterogeneity-oriented immunization measure based on individual heterogeneity and network topology factors. Lellis and Porfiri [30] designed an algorithm for detecting influential nodes in network dynamic systems using time series.…”

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

“…, where n is the number of nodes. The Shannon entropy and the weight of each method are computed as shown in Equation 13: Xin et al [168] proposed a network immunization method that considers the topology of the network as well as the heterogeneity of nodes. Specifically, the method estimates the influence of a node based on its structural centrality, extent of activity, and spreading ability.…”